Patent Publication Number: US-2023147270-A1

Title: Synchronized Spinning LIDAR and Rolling Shutter Camera System

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. patent application Ser. No. 17/186,121, filed Feb. 26, 2021, which is a continuation of U.S. patent application Ser. No. 16/556,683, filed Aug. 30, 2019, which is a continuation of U.S. patent application Ser. No. 15/719,366, filed Sep. 28, 2017. The foregoing applications are incorporated herein by reference. 
    
    
     BACKGROUND 
     Active sensors, such as light detection and ranging (LIDAR) sensors, radio detection and ranging (RADAR) sensors, and sound navigation and ranging (SONAR) sensors, among others, can scan an environment by emitting signals toward the environment and detecting reflections of the emitted signals. Passive sensors, such as image sensors and microphones among others, can detect signals originating from sources in the environment. 
     An example LIDAR sensor can determine distances to environmental features while scanning through a scene to assemble a “point cloud” indicative of reflective surfaces. Individual points in the point cloud can be determined, for example, by transmitting a laser pulse and detecting a returning pulse, if any, reflected from an object in the environment, and then determining a distance to the object according to a time delay between the transmission of the pulse and the reception of its reflection. Thus, a three-dimensional map of points indicative of locations of reflective features in the environment can be generated. 
     An example image sensor can capture an image of a scene viewable to the image sensor. For instance, the image sensor may include an array of complementary metal oxide semiconductor (CMOS) active pixel sensors, or other types of light sensors. Each CMOS sensor may receive a portion of light from the scene incident on the array. Each CMOS sensor may then output a measure of the amount of light incident on the CMOS sensor during an exposure time when the CMOS sensor is exposed to the light from the scene. With this arrangement, an image of the scene can be generated, where each pixel in the image indicates one or more values (e.g., colors, etc.) based on outputs from the array of CMOS sensors. 
     SUMMARY 
     In one example, a system includes a light detection and ranging (LIDAR) sensor that includes a transmitter and a receiver. The transmitter emits light pulses toward an environment of the LIDAR sensor. The receiver detects reflections of the emitted light pulses. The LIDAR sensor scans the environment based on rotation of the LIDAR sensor about an axis. The system also includes one or more cameras that detect external light originating from one or more external light sources. The one or more cameras together provide a plurality of rows of sensing elements. Each row of sensing elements is aligned with the axis of rotation of the LIDAR sensor. The system also includes a controller that operates the one or more cameras to obtain a sequence of image pixel rows. A first image pixel row in the sequence is indicative of a measurement of the external light by a first row of sensing elements during a first exposure time period. A second image pixel row in the sequence is indicative of a measurement of the external light by a second row of sensing elements during a second exposure time period. 
     In another example, a device includes a light detection and ranging (LIDAR) sensor that emits a plurality of light beams, directs the plurality of light beams toward a field-of-view (FOV) defined by a pointing direction of the LIDAR sensor, and detects reflections of the emitted light beams. The device also includes an image sensor that detects external light originating from one or more external light sources. The image sensor comprises an array of adjacent rows of sensing elements. A given row of sensing elements in the array is arranged according to an arrangement of given light beams directed by the LIDAR sensor for a given pointing direction of the LIDAR sensor. The device also includes an actuator that rotates the LIDAR sensor about an axis to adjust the pointing direction of the LIDAR sensor. An arrangement of the plurality of light beams emitted by the LIDAR sensor is based on at least the adjustment of the pointing direction. The device also includes a controller that operates the image sensor to obtain a sequence of image pixels in a particular order that is based on at least the arrangement of the plurality of light beams emitted by the LIDAR sensor. The sequence of image pixels is indicative of measurements by respective sensing elements in the array according to respective exposure time periods of the respective sensing elements to the external light. The respective exposure time periods are based on at least the particular order. 
     In yet another example, a method involves rotating a light detection and ranging (LIDAR) sensor about an axis to scan an environment of the LIDAR sensor. The LIDAR sensor emits light pulses toward the environment and detects reflections of the emitted light pulses. The method also involves obtaining a sequence of image pixel rows using one or more cameras that detect external light originating from one or more external light sources. The one or more cameras together provide a plurality of rows of sensing elements. Each row of sensing elements is aligned with the axis of rotation of the LIDAR sensor. A first image pixel row in the sequence is indicative of a measurement of the external light by a first row of sensing elements during a first exposure time period. A second image pixel row in the sequence is indicative of a measurement of the external light by a second row of sensing elements during a second exposure time period. 
     In still another example, a system comprises means for rotating a light detection and ranging (LIDAR) sensor about an axis to scan an environment of the LIDAR sensor. The LIDAR sensor emits light pulses toward the environment and detects reflections of the emitted light pulses. The system also comprises means for obtaining a sequence of image pixel rows using one or more cameras that detect external light originating from one or more external light sources. The one or more cameras together provide a plurality of rows of sensing elements. Each row of sensing elements is aligned with the axis of rotation of the LIDAR sensor. A first image pixel row in the sequence is indicative of a measurement of the external light by a first row of sensing elements during a first exposure time period. A second image pixel row in the sequence is indicative of a measurement of the external light by a second row of sensing elements during a second exposure time period. 
     These as well as other aspects, advantages, and alternatives will become apparent to those of ordinary skill in the art by reading the following detailed description with reference where appropriate to the accompanying drawings. Further, it should be understood that the description provided in this summary section and elsewhere in this document is intended to illustrate the claimed subject matter by way of example and not by way of limitation. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a simplified block diagram of a system, according to example 
       embodiments. 
         FIG.  2 A  illustrates a device that includes a rotating LIDAR sensor and a rolling shutter camera arrangement, according to example embodiments. 
         FIG.  2 B  is a cross-section view of the rolling shutter camera arrangement of  FIG.  2 A . 
         FIG.  2 C  is a conceptual illustration of an operation of the device of  FIG.  2 A . 
         FIG.  2 D  illustrates a top view of the device of  FIG.  2 A . 
         FIG.  2 E  illustrates another top view of the device of  FIG.  2 A . 
         FIG.  3    illustrates a cross-section view of another rolling shutter camera arrangement, according to example embodiments. 
         FIG.  4    is a flowchart of a method, according to example embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Exemplary implementations are described herein. It should be understood that the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any implementation or feature described herein as “exemplary” or “illustrative” is not necessarily to be construed as preferred or advantageous over other implementations or features. In the figures, similar symbols typically identify similar components, unless context dictates otherwise. The example implementations described herein are not meant to be limiting. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations. 
     I. Overview 
     Sensor fusion algorithms can be employed to merge data from multiple sensors, such as an image sensor and a LIDAR sensor for instance, to generate a representation of a scanned environment. For instance, a 3D representation of a scanned environment may indicate color information determined using an image sensor combined with other information (e.g., distance, depth, intensity, texture, reflected light pulse length, etc.) determined using a LIDAR sensor. 
     Example devices, systems, and methods herein relate to spatial and/or temporal synchronization of two or more sensors that sense a surrounding environment. One example device may include a rotating LIDAR sensor and one or more cameras. The one or more cameras may together provide an array of adjacent rows of image sensing elements. The rows in the array can be aligned with (e.g., substantially parallel to) an axis of rotation of the LIDAR sensor. For instance, if the LIDAR sensor rotates about a vertical axis, then each row of sensing elements could be arranged as a vertical line of sensing elements parallel to the vertical axis of the LIDAR sensor. 
     With this arrangement for instance, the device can improve synchronization of the timing and viewing directions associated with: (i) image pixels captured by the one or more cameras and (ii) reflected light pulses detected by the LIDAR sensor. 
     By way of example, the LIDAR sensor may have an axis of rotation that is substantially vertical and may be configured to emit light pulses in a vertical pattern (e.g., parallel to the axis of rotation of the LIDAR sensor) repeatedly as the LIDAR sensor rotates. The light pulses emitted in the vertical pattern can be emitted very rapidly in comparison to the rate of rotation of the LIDAR sensor. Thus, in this example, the vertical direction may be described as a “fast axis” of the LIDAR sensor, and the horizontal direction of rotation of the LIDAR sensor may be described as a “slow axis” of the LIDAR sensor. The fast axis of the LIDAR sensor may correspond to a vertical direction in a data point cloud generated using the LIDAR sensor, and the slow axis of the LIDAR sensor may correspond to a horizontal direction in the data point cloud, for example. 
     A controller of the device can operate the one or more cameras to obtain a sequence of image pixels in an order similar to an order in which the LIDAR sensor emits (and detects) light pulses. For instance, a first line of image pixels in the sequence can be measured using a first row of sensing elements (e.g., vertical row) in the array, in a similar order (e.g., top to bottom, etc.) as the order in which the light pulses are emitted by the LIDAR sensor. A second line of image pixels in the sequence (subsequent to the first line) can then be measured using an adjacent vertical row of sensing elements in the array, and so on. 
     Each line of image pixels in the sequence may be measured according to a respective exposure time period of a respective row of sensing elements in the array. For example, the first line of image pixels may indicate an amount of external light incident on the first row of sensing element during a first exposure time period, and the second subsequent line of image pixels may indicate an amount of the external light incident on the second (adjacent) row of sensing elements during a second exposure time period (which begins after the first exposure time period begins). In some implementations, the second exposure time period may begin after the first exposure time period begins but before the first exposure time period ends. Alternatively, in other implementations, the second exposure time period may begin after the first exposure time period ends. 
     Through this process, a “fast axis” of the one or more cameras may correspond to the fast axis of the LIDAR sensor, and a “slow axis” of the one or more cameras may correspond to the slow axis of the LIDAR sensor. For example, when mounting the one or more cameras in the example device, the camera(s) could be physically rotated (e.g., from a horizontal orientation to a vertical orientation, etc.) relative to the LIDAR sensor until the rows of sensing elements that are measured one after another (i.e., fast axis of the camera(s)) are parallel to the axis of rotation of the LIDAR sensor (i.e., fast axis of the LIDAR sensor). 
     In some implementations, the controller of the device may operate the one or more cameras to obtain the sequence of image pixels according to a timing configuration provided by the controller. The timing configuration, for instance, may be based on an orientation of the LIDAR sensor (e.g., viewing direction, pointing direction, angular position, etc.) about the axis of rotation of the LIDAR sensor. 
     For example, the controller can obtain image pixels measured using a row of image sensing elements that image a field-of-view (FOV) near or (at least partially) overlapping a current FOV of the LIDAR sensor. For instance, the row of image sensing elements may be configured to detect external light from a particular region of the environment. To synchronize collection of the image pixels with collection of LIDAR data, the device can expose the row of sensing elements to the external light during an exposure time period that includes a time when the LIDAR sensor is also scanning the particular region of the environment (e.g., when the FOV of the rotating LIDAR overlaps the particular region of the environment). By doing so, for instance, the device can improve the likelihood of matching the sequence of image pixels with corresponding light pulse reflections detected by the LIDAR sensor during a similar time frame and from a similar viewing direction. 
     Through this process, sensor data from the LIDAR sensor and the camera(s) can be more effectively combined. More generally, example implementations herein may improve accuracy and/or efficiency of computer operations related to combining sensor data from two (or more) sensors by synchronizing, in the time domain and/or the space domain, sensor data collection operations of the two (or more) sensors. 
     II. Example Sensors 
     Although example sensors described herein include LIDAR sensors and cameras (or image sensors), other types of sensors are possible as well. A non-exhaustive list of example sensors that can be alternatively employed herein without departing from the scope of the present disclosure includes RADAR sensors, SONAR sensors, sound sensors (e.g., microphones, etc.), motion sensors, temperature sensors, pressure sensors, etc. 
     To that end, example sensors herein may include active sensors that emit a signal (e.g., a sequence of pulses or any other modulated signal) based on modulated power provided to the sensor, and then detects reflections of the emitted signal from objects in the surrounding environment. Alternatively or additionally, example sensors herein may include passive sensors (e.g., cameras, microphones, antennas, pressure sensors, etc.) that detect external signals (e.g., background signals, etc.) originating from external source(s) in the environment. 
     Referring now to the figures,  FIG.  1    is a simplified block diagram of a system  100  that includes synchronized sensors, according to an example embodiment. As shown, system  100  includes a power supply arrangement  102 , a controller  104 , one or more sensors  106 , one or more sensors  108 , a rotating platform  110 , one or more actuators  112 , a stationary platform  114 , a rotary link  116 , a housing  118 , and a display  140 . 
     In other embodiments, system  100  may include more, fewer, or different components. Additionally, the components shown may be combined or divided in any number of ways. For example, sensor(s)  108  can be implemented as a single physical component (e.g., camera ring). Alternatively, for example, sensor(s)  108  can be implemented as an arrangement of separate physical components. Other examples are possible. Thus, the functional blocks of  FIG.  1    are illustrated as shown only for convenience in description. Other example components, arrangements, and/or configurations are possible as well without departing from the scope of the present disclosure. 
     Power supply arrangement  102  may be configured to supply, receive, and/or distribute power to various components of system  100 . To that end, power supply arrangement  102  may include or otherwise take the form of a power source (e.g., battery cells, etc.) disposed within system  100  and connected to various components of system  100  in any feasible manner, so as to supply power to those components. Additionally or alternatively, power supply arrangement  102  may include or otherwise take the form of a power adapter configured to receive power from one or more external power sources (e.g., from a power source arranged in a vehicle to which system  100  is mounted, etc.) and to transmit the received power to various components of system  100 . 
     Controller  104  may include one or more electronic components and/or systems arranged to facilitate certain operations of system  100 . Controller  104  may be disposed within system  100  in any feasible manner. In one embodiment, controller  104  may be disposed, at least partially, within a central cavity region of rotary link  116 . In another embodiment, one or more functions of controller  104  can be alternatively performed by one or more physically separate controllers that are each disposed within a respective component (e.g., sensor(s)  106 ,  108 , etc.) of system  100 . 
     In some examples, controller  104  may include or otherwise be coupled to wiring used for transfer of control signals to various components of system  100  and/or for transfer of data from various components of system  100  to controller  104 . Generally, the data that controller  104  receives may include sensor data based on detections of light by LIDAR  106  and/or camera(s)  108 , among other possibilities. Moreover, the control signals sent by controller  104  may operate various components of system  100 , such as by controlling emission and/or detection of light or other signal by sensor(s)  106  (e.g., LIDAR, etc.), controlling image pixel capture rate or times via a camera (e.g., included in sensor(s)  108 ), and/or controlling actuator(s)  112  to rotate rotating platform  110 , among other possibilities. 
     To that end, in some examples, controller  104  may include one or more processors, data storage, and program instructions (stored in the data storage) executable by the one or more processors to cause system  100  to perform the various operations described herein. In some instances, controller  104  may communicate with an external controller or the like (e.g., a computing system arranged in a vehicle, robot, or other mechanical device to which system  100  is mounted) so as to help facilitate transfer of control signals and/or data between the external controller and the various components of system  100 . 
     Additionally or alternatively, in some examples, controller  104  may include circuitry wired to perform the various functions described herein. Additionally or alternatively, in some examples, controller  104  may include one or more special purpose processors, servos, or other types of controllers. For example, controller  104  may include a proportional-integral-derivative (PID) controller or other control loop feedback apparatus that operates actuator(s)  112  to modulate rotation of rotating platform  110  according to a particular frequency or phase. Other examples are possible as well. 
     Sensors  106  and  108  can optionally include one or more sensors, such as LIDARs, cameras, gyroscopes, accelerometers, encoders, microphones, RADARs, SONARs, thermometers, etc., that scan a surrounding environment of system  100 . 
     Sensor(s)  106  may include any device configured to scan a surrounding environment by emitting a signal and detecting reflections of the emitted signal. For instance, sensor(s)  106  may include any type of active sensor. To that end, as shown, sensor  106  includes a transmitter  120  and a receiver  122 . In some implementations, sensor  106  may also include one or more optical elements  124 . 
     Transmitter  120  may be configured to transmit a signal toward an environment of system  100 . 
     In a first example, where sensor  106  is configured as a LIDAR sensor, transmitter  120  may include one or more light sources (not shown) that emit one or more light beams and/or pulses having wavelengths within a wavelength range. The wavelength range could, for example, be in the ultraviolet, visible, and/or infrared portions of the electromagnetic spectrum depending on the configuration of the light sources. In some examples, the wavelength range can be a narrow wavelength range, such as provided by lasers and/or some light emitting diodes. In some examples, the light source(s) in transmitter  120  may include laser diodes, diode bars, light emitting diodes (LEDs), vertical cavity surface emitting lasers (VCSELs), organic light emitting diodes (OLEDs), polymer light emitting diodes (PLEDs), light emitting polymers (LEPs), liquid crystal displays (LCDs), microelectromechanical systems (MEMS), fiber lasers, and/or any other device configured to selectively transmit, reflect, and/or emit light to provide a plurality of emitted light beams and/or pulses. 
     In a second example, where sensor  106  is configured as an active infrared (IR) camera, transmitter  120  may be configured to emit IR radiation to illuminate a scene. To that end, transmitter  120  may include any type of device (e.g., light source, etc.) configured to provide the IR radiation. 
     In a third example, where sensor  106  is configured as a RADAR sensor, transmitter  120  may include one or more antennas configured to emit a modulated radio-frequency (RF) signal toward an environment of system  100 . 
     In a fourth example, where sensor  106  is configured as a SONAR sensor, transmitter  120  may include one or more acoustic transducers, such as piezoelectric transducers, magnetostrictive transducers, electrostatic transducers, etc., configured to emit a modulated sound signal toward an environment of system  100 . In some implementations, the acoustic transducers can be configured to emit sound signals within a particular wavelength range (e.g., infrasonic, ultrasonic, etc.). Other examples are possible as well. 
     Receiver  122  may include one or more detectors configured to detect reflections of the signal emitted by transmitter  120 . 
     In a first example, where sensor  106  is configured as a RADAR sensor, receiver  122  may include one or more antennas configured to detect reflections of the RF signal transmitted by transmitter  120 . To that end, in some implementations, the one or more antennas of transmitter  120  and receiver  122  can be physically implemented as the same physical antenna structures. 
     In a second example, where sensor  106  is configured as a SONAR sensor, receiver  122  may include one or more sound sensors (e.g., microphones, etc.) that are configured to detect reflections of the sound signals emitted by transmitter  120 . 
     In a third example, where sensor  106  is configured as an active IR camera, receiver  122  may include one or more light detectors (e.g., active pixel sensors, etc.) that are configured to detect a source wavelength of IR light transmitted by transmitter  120  and reflected off a scene toward receiver  122 . 
     In a fourth example, where sensor  106  is configured as a LIDAR sensor, receiver  122  may include one or more light detectors (e.g., photodiodes, avalanche photodiodes, etc.) that are arranged to intercept and detect reflections of the light pulses emitted by transmitter  120  and reflected from one or more objects in a surrounding environment of system  100 . To that end, receiver  122  may be configured to detect light having wavelengths in the same wavelength range as the light emitted by transmitter  120 . In this way, for instance, sensor  106  (e.g., LIDAR) may distinguish reflected light pulses originated by transmitter  120  from other light originating from external light sources in the environment. 
     In some instances, receiver  122  may include a photodetector array, which may include one or more detectors each configured to convert detected light (e.g., in the wavelength range of light emitted by transmitter  120 ) into an electrical signal indicative of the detected light. In practice, such a photodetector array could be arranged in one of various ways. For instance, the detectors can be disposed on one or more substrates (e.g., printed circuit boards (PCBs), flexible PCBs, etc.) and arranged to detect incoming light. Also, such a photodetector array could include any feasible number of detectors aligned in any feasible manner. Additionally, the detectors in the array may take various forms. For example, the detectors may take the form of photodiodes, avalanche photodiodes (e.g., Geiger mode and/or linear mode avalanche photodiodes), silicon photomultipliers (SiPMs), phototransistors, cameras, active pixel sensors (APS), charge coupled devices (CCD), cryogenic detectors, and/or any other sensor of light configured to receive focused light having wavelengths in the wavelength range of the emitted light. 
     In some implementations, sensor  106  (e.g., in a LIDAR configuration) can select or adjust a horizontal scanning resolution by changing a rate of rotation of the LIDAR and/or adjusting a pulse rate of light pulses emitted by transmitter  120 . As a specific example, transmitter  120  can be configured to emit light pulses at a pulse rate of 15,650 light pulses per second. In this example, LIDAR  106  may be configured to rotate at 10 Hz (i.e., ten complete 360° rotations per second). As such, receiver  122  can detect light with a 0.23° horizontal angular resolution. Further, the horizontal angular resolution of 0.23° can be adjusted by changing the rate of rotation of LIDAR  106  or by adjusting the pulse rate. For instance, if LIDAR  106  is instead rotated at 20 Hz, the horizontal angular resolution may become 0.46°. Alternatively, if transmitter  120  emits the light pulses at a rate of 31,300 light pulses per second while maintaining the rate of rotation of 10 Hz, then the horizontal angular resolution may become 0.115°. Other examples are possible as well. Further, in some examples, LIDAR  106  can be alternatively configured to scan a particular range of views within less than a complete 360° rotation of LIDAR  106 . 
     Optical element(s)  124  can be optionally included in or otherwise coupled to transmitter  120  and/or receiver  122 . In one example (e.g., where sensor  106  includes a LIDAR sensor), optical element(s)  124  can be arranged to direct light from a light source in transmitter  120  toward the environment. In another example, optical element(s)  124  can be arranged to focus and/or guide light from the environment toward receiver  122 . As such, optical element(s)  124  may include any feasible combination of mirror(s), waveguide(s), light filters, lens(es), or any other optical components arranged to guide propagation of light through physical space and/or adjust certain light characteristics. For instance, optical elements  124  may include a light filter arranged to reduce or prevent light having wavelengths outside the wavelength range of the light emitted by transmitter  120  from propagating toward receiver  122 . With such arrangement for instance, the light filter can reduce noise due to background light propagating from the scanned environment and originating from an external light source different than light sources of transmitter  120 . 
     Sensor(s)  108  may include any type of sensor configured to scan the surrounding environment. As shown, sensors  108  include an array of sensing elements  128 . Further, as shown, sensors  108  can optionally include one or more optical elements  130 . 
     In some examples, sensor(s)  108  may include active sensors (e.g., LIDAR, RADAR, SONAR, etc.) that transmit signals and detect reflections thereof. Thus, although not shown, sensors  108  may include a transmitter and a receiver that are similar to, respectively, transmitter  120  and receiver  122 . In other examples, sensor(s)  108  may include passive sensors (e.g., microphones, cameras, image sensors, thermometers, etc.) that detect external signals originating from one or more external sources. 
     In a first example, where sensor  108  is configured as a sound sensor, sensing elements  128  may include an array of microphones that each detect sounds (e.g., external signals) incident on the respective microphones in the array. 
     In a second example, where sensor(s)  108  are configured as one or more cameras, the camera(s) may include any camera (e.g., a still camera, a video camera, etc.) configured to capture images of the environment in which system  100  is located. For example, a camera of sensor  108  may include any imaging device that detects and provides data indicative of an image. For instance, sensing elements  128  may include one or more arrangements of light sensing elements that each provide a measure of light incident thereon. To that end, sensing elements  128  may include charge-coupled devices (CCDs), active pixel sensors, complementary metal-oxide-semiconductor (CMOS) photodetectors, N-type metal-oxide-semiconductor (NMOS) photodetectors, among other possibilities. 
     Further, in some examples, data from sensing elements  128  can be combined according to the arrangement of the sensing elements  128  to generate an image. In one example, data from a two-dimensional (2D) array of sensing elements may correspond to a 2D array of image pixels in the image. In another example, a 3D arrangement of sensing elements (e.g., sensing elements arranged along a curved surface) can be similarly used to generate a 2D array of image pixels in the image. Other examples are possible as well. 
     In some examples, a sensing element can optionally include multiple adjacent light detectors (or detectors of other types of signals), where each detector is configured to detect light (or other signal) having a particular wavelength or wavelength range. For instance, an image pixel may indicate color information (e.g., red-green-blue or RGB) based on a combination of data from a first detector that detects an intensity of red light, a second detector that detects an intensity of green light, and a third detector that detects an intensity of blue light. Other examples are possible as well. 
     In one embodiment, sensor(s)  108  may be configured to detect visible light propagating from the scene. Further, in this embodiment, receiver  122  of sensor  106  (e.g., LIDAR receiver) may be configured to detect invisible light (e.g., infrared, etc.) within a wavelength range of light emitted by transmitter  120 . In this embodiment, system  100  (or controller  104 ) can then combine data from sensor  106  (e.g., LIDAR) with data from sensor  108  (e.g., camera) to generate a colored three-dimensional (3D) representation (e.g., point cloud) of the scanned environment. 
     In some examples, sensor(s)  108  may comprise a plurality of cameras (e.g., a camera ring) disposed in a circular arrangement around an axis of rotation of sensor  106  (e.g., LIDAR). For example, a first camera may be arranged to image a first field-of-view (FOV) of the environment that at least partially overlaps a range of pointing directions of sensor  106  as sensor  106  rotates about the axis (or as the signals transmitted by transmitter  120  are otherwise steered to different pointing directions about the axis). Further, a second camera adjacent to and/or overlapping the first camera may image a second FOV adjacent to the first FOV of the first camera, and so on. In this way, for instance, system  100  may be configured to capture a sequence of images of the respective FOVs simultaneously (and/or synchronously) with a scan of the environment by sensor  106  as sensor  106  rotates about the axis. 
     In some examples, sensor(s)  108  may be configured to operate in a rolling shutter mode. 
     In a first example, where sensor(s)  108  include a microphone array, each output from a microphone in the array may be associated with a respective exposure time period of a corresponding sensing element (e.g., microphone) to external sounds incident on sensor  108 . 
     In a second example, where sensor(s)  108  include one or more cameras, each pixel or group of pixels output by the camera(s) may be associated with a respective exposure time period of a corresponding sensing element or group of sensing elements to external light. By way of example, camera(s)  108  may together provide an array of adjacent rows of sensing elements  128 . Further, camera(s)  108  can be configured to output a sequence of image pixels that correspond to measurements of the external light by corresponding sensing elements in the array. For example, camera(s)  108  may output a first row of image pixels based on data from a first row of sensing elements in the array, followed by a second row of image pixels based on data from a second adjacent row of sensing elements in the array, and so on. 
     In this way, the first image pixel row may be associated with a first exposure time period during which the first row of sensing elements was exposed to light, the second image pixel row may be associated with a second exposure time period during which the second adjacent row of sensing elements was exposed to light, etc. The first exposure time period may begin before the second exposure time period begins. For instance, after a time delay from a start time of the first exposure time period (and optionally before the first exposure time period lapses), camera(s)  108  may start exposing the second adjacent row of sensing elements. Additionally, the first exposure time period may end before the second exposure time period ends. For instance, controller  104  may read outputs from the first row of sensing elements after the first exposure time period ends and while the second row of sensing elements is still being exposed to the external light, and then read outputs from the second row of sensing elements after the second exposure period ends and while a third row of sensing elements is still being exposed to the external light, and so on. 
     In some examples, where sensor  106  includes an image sensor, system  100  may be configured to select the order in which the sequence of image pixels are obtained from sensing elements  128  in the rolling shutter mode based on an order in which transmitter  120  is emitting light pulses (or other signals). For example, a given row of sensing elements in the array of sensing elements  128  may be aligned (e.g., parallel, etc.) with the axis of rotation of a LIDAR (e.g., sensor  106 ). For instance, if the axis of rotation of the LIDAR is a vertical axis, then the given row may correspond to a vertical row of sensing elements (e.g., vertical linear arrangement parallel to the axis of rotation of the LIDAR). Further, transmitter  120  may be configured to output a plurality of light beams in an arrangement of one or more vertical lines repeatedly as the LIDAR (e.g., sensor  106 ) rotates about the axis. As such, for example, sensor(s)  108  (e.g., camera(s)) may output a first row of image pixels using a first row of sensing elements that are arranged similarly (e.g., vertically, etc.) to the arrangement of the plurality of light beams emitted by transmitter  120 . Next, camera(s)  108  may then output a second row of image pixels using a second adjacent row of sensing elements in the direction of the rotation of the LIDAR (or other sensor  106 ). Thus, for instance, the second row of image pixels may be aligned with a second vertical line of light beams emitted by transmitter  120  after sensor  106  rotates toward the second row of sensing elements, and so on. 
     By scanning vertical rows of sensing elements one after another, for instance, the sequence of image pixels obtained from camera(s)  108  may include a sufficient number of pixels that were captured at times (and from viewing directions) that are similar to the times and directions of LIDAR light pulses (or other signals) emitted by transmitter  120  (e.g., as transmitter  120  rotates about a vertical axis). Whereas, for instance, if the camera(s) (e.g., sensor(s)  108 ) instead captured the sequence of image pixels using a first horizontal row of sensing elements followed by a second horizontal row of sensing elements and so on, then fewer image pixels may be captured at times (and from viewing directions) that are similar to the times and directions of the LIDAR light pulses. 
     Optical element(s)  130  may include any combination of optical components such as lens(es), mirror(s), waveguide(s), light filter(s) or any other type of optical component similarly to optical element(s)  124 . Further, optical elements  130  can be arranged to focus, direct, and/or adjust light characteristics of incident light for propagation toward sensing elements  128 . Further, where sensor(s)  108  include a plurality of cameras for instance, optical element(s)  130  may include a plurality of respective camera lenses that focus external light onto respective image sensors of the cameras. 
     In some examples, optical element(s)  130  may include one or more light filters that selectively transmit particular wavelengths of light toward one or more particular sensing elements of sensor  106 . 
     In a first example, optical element(s)  130  may include one or more light filters that attenuate light wavelengths of light emitted by transmitter  120 . With this arrangement, for instance, system  100  can reduce noise measurements (by sensing element(s)  128 ) that are associated with the high intensity of light pulses (or other signals) emitted by transmitter  120 . 
     In a second example, sensor  108  may include color image sensors (e.g., Bayer filter sensor, layered pixel sensor array, etc.) configured to indicate colors of incident light. In this example, optical element(s)  130  may include a color filter array, where each color filter of the array transmits red, green, or blue light to a particular sensing element adjacent to the color filter (and attenuates other wavelengths of light). System  100  can then generate (e.g., by combining outputs from multiple sensing elements that sense light having different colors) image pixels that indicate color information (e.g., red, green, and blue, etc.). 
     In a third example, optical element(s)  130  may include one or more filters that attenuate wavelengths of the light (or other signal) emitted by transmitter  120  and one or more other filters that allow transmission of these wavelengths. For instance, optical element(s)  130  may include a color filter array that includes green, red, and blue light filters. In this instance, a relatively large number of the color filters can be configured to attenuate the wavelengths of the emitted light of transmitter  120  to reduce the effects of the high intensity signals emitted by transmitter  120 . Further, a relatively smaller number of the color filters (e.g., one or more of the green light filters, etc.) can be configured to (at least partially) allow transmission of wavelengths of the light (or other signal) emitted by transmitter  120 . With this arrangement, the high intensity light of transmitter  120  (reflecting off objects in the environment of system  100 ) can be used to illuminate one or more sensing elements in dark external light conditions (e.g., night time). 
     Rotating platform  110  may be configured to rotate about an axis. For example, sensor  106  (and/or transmitter  120  and receiver  122  thereof) may be supported (directly or indirectly) by rotating platform  110  such that each of these components moves relative to the environment in response to rotation of rotating platform  110 . In particular, each of these components could be rotated (simultaneously) relative to an axis so that sensor  106  may obtain information from various directions. In some examples, the axis of rotation of rotating platform  110  is vertical and a pointing direction of sensor  106  can be adjusted horizontally by the rotation of rotating platform  110  about its vertical axis of rotation. Rotating platform  110  can be formed from any solid material suitable for supporting one or more components (e.g., sensor  106 ) mounted thereon. 
     In order to rotate platform  110  in this manner, one or more actuators  112  may actuate rotating platform  110 . To that end, actuators  112  may include motors, pneumatic actuators, hydraulic pistons, and/or piezoelectric actuators, among other possibilities. 
     With this arrangement, controller  104  could operate actuator  112  to rotate rotating platform  110  in various ways so as to obtain information about the environment. In one example, rotating platform  110  could be rotated in either direction. In another example, rotating platform  110  may carry out complete revolutions such that sensor  106  (e.g., LIDAR) provides a 360° horizontal FOV of the environment. Moreover, rotating platform  110  may rotate at various frequencies so as to cause sensor  106  to scan the environment at various refresh rates and/or scanning resolutions. 
     Alternatively or additionally, system  100  may be configured to adjust the pointing direction of the emitted signal (emitted by transmitter  120 ) in various ways. In some examples, signal sources (e.g., light sources, antennas, acoustic transducers, etc.) of transmitter  120  can be operated according to a phased array configuration or other type of beam steering configuration. 
     In a first example, where sensor  106  is configured as a LIDAR sensor, light sources in transmitter  120  can be coupled to phased array optics (e.g., optical elements  124 ) that control the phase of light waves emitted by the light sources. For instance, controller  104  can be configured to adjust the phased array optics (e.g., phased array beam steering) to change the effective pointing direction of a light signal emitted by transmitter  120  (e.g., even if rotating platform  110  is not rotating). 
     In a second example, where sensor  106  is configured as a RADAR sensor, transmitter  120  may include an array of antennas, and controller  104  can provide respective phase-shifted control signals for each individual antenna in the array to modify a pointing direction of a combined RF signal from the array (e.g., phased array beam steering). 
     In a third example, where sensor  106  is configured as a SONAR sensor, transmitter  120  may include an array of acoustic transducers, and controller  104  can similarly operate the array of acoustic transducers (e.g., via phase-shifted control signals, etc.) to achieve a target pointing direction of a combined sound signal emitted by the array (e.g., even if the rotating platform  110  is not rotating, etc.). 
     In other examples, the pointing direction of sensor(s)  106  can be controlled using a deforming flexible structure (e.g., MEMs, etc.) that can be deformed in response to a control signal from controller  104  to adjust a steering direction of the signals emitted by transmitter  120 . Other examples are possible. 
     Stationary platform  114  may take on any shape or form and may be configured for coupling to various structures, such as to a top of a vehicle for example. Also, the coupling of stationary platform  114  may be carried out via any feasible connector arrangement (e.g., bolts and/or screws). In this way, system  100  could be coupled to a structure so as to be used for various purposes, such as those described herein. In one example, sensor(s)  108  can be coupled to stationary platform  114 . In this example, sensor(s)  108  can remain stationary relative to the rotational motion of sensor(s)  106  (or the otherwise changing beam directions of signals emitted by transmitter  120 ). In another example, sensor(s)  108  can be mounted to another physical structure different than stationary platform 
     Rotary link  116  directly or indirectly couples stationary platform  114  to rotating platform  110 . To that end, rotary link  116  may take on any shape, form and material that provides for rotation of rotating platform  110  about an axis relative to stationary platform  114 . In some examples, rotary link  116  may take the form of a shaft or the like that rotates based on actuation from actuator  112 , thereby transferring mechanical forces from actuator  112  to rotating platform  110 . In one implementation, rotary link  116  may have a central cavity in which one or more components of system  100  may be disposed. In some examples, rotary link  116  may also provide a communication link for transferring data and/or instructions between stationary platform  114  and rotating platform  110  (and/or components thereon such as sensor(s)  106 , etc.). 
     Housing  118  may take on any shape, form, and material and may be configured to house one or more components of system  100 . In one example, housing  118  can be a dome-shaped housing. Further, in some examples, housing  118  may be composed of a material that is at least partially non-transparent, which may allow for blocking of at least some light from entering the interior space of housing  118  and thus help mitigate thermal and noise effects of ambient light on one or more components of system  100 . Other configurations of housing  118  are possible as well. In some implementations, housing  118  may be coupled to rotating platform  110  such that housing  118  is configured to rotate about the above-mentioned axis based on rotation of rotating platform  110 . In such implementations, sensor(s)  106  may rotate together with housing  118 . In other implementations, housing  118  may remain stationary while sensor(s)  106  rotate within housing  118 . System  100  could also include multiple housings similar to housing  118  for housing certain sub-systems or combinations of components of system  100 . For example, system  100  may include a first housing for sensor(s)  106  and a separate housing for sensor(s)  108 . Other examples are possible as well. 
     Display  140  can optionally be included in system  100  to display information about one or more components of system  100 . For example, controller  104  may operate display  140  to display images captured using a camera (e.g., sensor  108 ), a representation (e.g., 3D point cloud, etc.) of an environment of system  100  indicated by LIDAR data from sensor  106 , and/or a representation of the environment based on a combination of the data from sensors  106  and  108  (e.g., colored point cloud, images with superimposed temperature indicators, etc.). To that end, display  140  may include any type of display (e.g., liquid crystal display, LED display, cathode ray tube display, projector, etc.). Further, in some examples, display  140  may have a graphical user interface (GUI) for displaying and/or interacting with images captured by sensor  108 , LIDAR data captured using sensor  106 , and/or any other information about the various components of system  100  (e.g., power remaining via power supply arrangement  102 ). For example, a user can manipulate the GUI to adjust a scanning configuration of sensors  106  and/or  108  (e.g., scanning refresh rate, scanning resolution, etc.). 
     It is noted that the various components of system  100  can be combined or separated into a wide variety of different arrangements. For example, although sensors  106  and  108  are illustrated as separate components, one or more components of sensors  106  and  108  can alternatively be physically implemented within a single device. Thus, this arrangement of system  100  is described for exemplary purposes only and is not meant to be limiting. 
       FIG.  2 A  illustrates a device  200  that includes a rotating LIDAR sensor  206  and a camera ring  208 , according to example embodiments. As shown, device  200  includes a LIDAR  206 , camera ring  208  (e.g., rolling shutter camera arrangement, etc.), a rotating platform  210 , a stationary platform  214 , a housing  218 , a LIDAR lens  224 , and camera lenses  230 ,  232 ,  234  which may be similar, respectively, to sensor(s)  106 , sensor(s)  108 , rotating platform  110 , stationary platform  114 , housing  118 , optical element  124 , and optical elements  130 , for example. 
     As shown, light beams  250  emitted by LIDAR  206  propagate from lens  224  along a pointing direction of LIDAR  206  toward an environment of LIDAR  206 , and reflect off one or more objects (not shown) in the environment as reflected light  260 . Further, as shown, LIDAR  206  may then receive reflected light  290  (e.g., through lens  224 ). Thus, for instance, LIDAR  206  may provide data (e.g., data point cloud, etc.) indicating distances between the one or more objects and the LIDAR  206  based on detection(s) of the reflected light  290 , similarly to the discussion above for sensor  106 . 
     Further, as shown, each camera of camera ring  208  may receive and detect a respective portion of external light  270  incident on the respective camera. To that end, external light  270  may include light originating from one or more external light sources, such as the sun, a street lamp, among other possibilities. For example, external light  270  may include light propagating directly from an external light source toward camera lenses  230 ,  232 , and/or  234 . Alternatively or additionally, external light  270  may include light originating from an external light source and reflecting off one or more objects (not shown) in the environment of device  200  before propagating toward lenses  230 ,  232 , and/or  234 . Thus, for example, the cameras of camera ring  208  may generate one or more images of the environment based on external light  270 . Further, each image generated by a particular camera may correspond to a particular FOV of the particular camera relative to device  200 . 
     To that end, in some examples, camera ring  208  may include a plurality of cameras that are arranged in a ring formation (e.g., circular arrangement, oval arrangement, etc.) relative to one another. Each camera of the plurality can be positioned (e.g., mounted to device  200  and/or camera ring  208 ) at a particular angle and/or orientation. Thus, for instance, a FOV of a first camera may be adjacent to and/or partially overlapping FOVs of two other adjacent cameras. With this arrangement for instance, images from the individual cameras can be combined into an image of a 360-degree FOV of device  200 . Further, during assembly or calibration of device  200  for instance, the respective angle and/or orientation of each camera can be adjusted to reduce or prevent blind spots (e.g., regions of the surrounding environment that are not within FOVs of all the cameras in camera ring  208 ). For example, the respective FOVs of two adjacent cameras can be aligned (e.g., by moving, rotating, and/or otherwise adjusting relative mounting positions of the two cameras, etc.) such that a region of the environment between the FOVs of the two cameras (e.g., “blind spot”) is less than a threshold distance from device  200 . 
     To facilitate this, in one implementation, camera ring  208  could optionally include a housing (e.g., ring-shaped, etc.) having one or more indentations that receive and/or support the cameras at particular respective mounting positions (e.g., angle, orientation, etc.). In another implementation, an example system (e.g., system  100 , a calibration system, etc.) 
     may be configured to compare images captured by the cameras, and to determine, based on the comparison, alignment offsets that achieve respective target FOVs for the respective cameras. The example system may also include and/or operate a robotic arm, an actuator, and/or any other alignment apparatus to adjust the positions of the cameras in camera ring  208  according the determined alignment offsets. Other examples are possible. 
     In some examples, device  200  (or another computing device coupled thereto) may operate the cameras of camera ring  208  and/or process the captured images therefrom (e.g., combine portions of the captured images, etc.) to form a cohesive circular vision of the environment of device  200 . For example, a computing system (not shown) of device  200  or another device may match features in images captured by camera ring  208  to generate a combined image that spans a combination of the FOVs of the cameras. 
     In one implementation, lens  230  may focus light from a first 90-degree FOV of device  200 , lens  232  may focus light from a second adjacent 90-degree FOV, and so on. The first FOV could optionally partially overlap the first FOV. In other implementations, the FOV imaged by each camera may be more or less than 90 degrees. Further, in line with the discussion above, an image captured by any of the cameras in camera ring  208  may indicate various types of information such as light intensities for different wavelengths (e.g., colors, etc.) in external light  270 , among other examples. 
     In some examples, LIDAR  206  (and/or housing  218 ) can be configured to have a substantially cylindrical shape and to rotate about axis  242 , based on rotation of rotating platform  210  that supports LIDAR  206  for instance. Further, in some examples, the axis of rotation  242  may be substantially vertical. Thus, for instance, by rotating LIDAR  206  about axis  242 , device  200  (and/or a computing system that operates device  200 ) can determine a three-dimensional map based on data from LIDAR  206 ) of a 360-degree view of the environment of device  200 . Additionally or alternatively, in some examples, device  200  can be configured to tilt the axis of rotation of rotating platform  210  (relative to stationary platform  214 ), thereby adjusting the FOV of LIDAR  206 . For instance, rotating platform  210  may include a tilting platform that tilts in one or more directions. 
     In some examples, as shown, LIDAR lens  224  can have an optical power to both collimate (and/or direct) emitted light beams  250  toward an environment of LIDAR  206 , and focus reflected light  260  received from the environment onto a LIDAR receiver (not shown) of LIDAR  206 . In one example, lens  224  has a focal length of approximately  120  mm. Other example focal lengths are possible. By using the same lens  224  to perform both of these functions, instead of a transmit lens for collimating and a receive lens for focusing, advantages with respect to size, cost, and/or complexity can be provided. Alternatively however, LIDAR  206  may include separate transmit and receive lenses. Thus, although not shown, LIDAR  206  can alternatively include a transmit lens that directs emitted light  250  toward the environment, and a separate receive lens that focuses reflected light  260  for detection by a receiver of LIDAR  206 . 
     It is noted that device  200  may include more, fewer, or different components than those shown, and one or more of the components shown may be combined or separated in different ways. In one example, instead of multiple camera lenses  230 ,  232 ,  234 , device  200  could alternatively include a single camera lens that extends around a circumference of camera ring  208 . In another example, although camera ring  208  is shown to be coupled to stationary platform  214 , camera ring  208  can alternatively be implemented as a separate physical structure. In yet another example, camera ring  208  can be positioned above LIDAR  206 , without being rotated by rotating platform  214 . In still another example, camera ring  208  may include more or fewer cameras than shown. Other examples are possible. 
       FIG.  2 B  illustrates a cross-section view of camera ring  208 , according to an example embodiment. In the cross-section view of  FIG.  2 B , axis  242  (i.e., axis of rotation of LIDAR  206 ) extends through the page. As shown, camera ring  208  includes four cameras  208   a,    208   b,    208   c,    208   d  that are arranged around axis of rotation  242 . Thus, in the example shown, each of the cameras may be configured to image a respective 90-degree FOV of the environment of device  200 . However, in other embodiments, camera ring  208  may include fewer or more cameras than shown. In one particular embodiment, camera ring  208  may alternatively include eight cameras, where each camera is coupled to a respective lens that focuses light from (at least) a respective 45-degree FOV of the environment onto an image sensor of the camera. Other examples are possible. Thus, camera ring  208  may have a wide variety of different configurations and thus the configuration shown includes four cameras only for convenience in description. 
     Further, as shown, camera  208   a  includes lens  230  that focuses a first portion of external light (e.g., light  270 ) from the environment of device  200  onto an image sensor  226  of camera  208   a.  Further, as shown, camera  208   b  includes lens  232  that focuses a second portion of the external light onto an image sensor  246  of camera  232 . Similarly, cameras  208   c  and  208   d  may be configured to focus respective portions of the external light onto respective image sensors of the cameras. 
     Further, as shown, each image sensor may include an array of sensing elements similar to sensing elements  128  for example. For instance, image sensor  226  of camera  208   a  may include an array of adjacent rows of sensing elements, exemplified by sensing elements  228   a - 228   f  (which may be similar to sensing elements  128  for example). By way of example, a first row of sensing elements in image sensor  226  may include sensing element  228   a  and one or more other sensing elements (not shown) that are vertically arranged through the page (e.g., parallel to axis  242 ). Further, a second row of sensing elements adjacent to the first row may include sensing element  228   b  and one or more other sensing elements (not shown) that are vertically arranged through the page, and so on. 
     In this way, for example, cameras  208   a,    208   b,    208   c,    208   d  may together provide an array of adjacent rows of sensing elements that are arranged around axis  242 , so as to be able to image various corresponding portions of a 360-degree (horizontal) FOV around device  200 . For instance, a given row of sensing elements in image sensor  246  of camera  204   b  may include sensing element  248   a  (and one or more other sensors arranged parallel to axis  242  through the page). Further, in this instance, the given row of sensing elements in camera  208   b  may also be adjacent to a row of sensing elements in camera  208   a  that includes sensing element  228   f.  Thus, in an example scenario, the sequence of image pixels obtained from camera ring  208  may include a row of image pixels obtained using data from the row of sensing elements that includes sensing element  228   f,  followed by a row of image pixels obtained using data from the row of sensing elements that includes sensing element  248   a.    
     It is noted that the number of rows of sensing elements in each of the image sensors (e.g., sensors  226 ,  246 , etc.) is illustrated as shown only for convenience in description. However, in some embodiments, image sensor  226  (and/or  246 ) may include more or fewer rows of sensing elements than shown. In one particular embodiment, image sensor  226  may alternatively include 3000 rows of sensing elements, and each row may include 1000 sensing elements (extending through the page). In this embodiment, camera  208   a  may thus be configured to output a 3000×1000 pixel image. Further, in this embodiment, camera  208   a  may be configured to capture images at a rate of 60 Hz. Other camera configuration parameters are possible as well. 
     It is noted that the sizes, shapes, and positions of the various components of device  200  are not necessarily to scale, but are illustrated as shown only for convenience in description. In one example, the sizes of the lenses  230 ,  232 ,  234 ,  236 , and sensors  226 ,  246 , etc., shown in  FIG.  2 B  may be different than the sizes shown. In another example, the distance between lens  230  and image sensor  226  may be different than the distance shown. In one embodiment, the distance from lens  230  to sensor  226  may correspond to approximately twice the diameter of lens  230 . However, in other embodiments, image sensor  226  and camera lens  230  may have other sizes, shapes, and/or positions relative to one another. 
       FIG.  2 C  is a conceptual illustration of an operation of device  200 , according to an example embodiment. In the illustration of  FIG.  2 C , the sensing elements of image sensor  226  of camera  208   a  are in the plane of the page. It is noted that some of the components of device  200 , such as camera lens  230  and LIDAR  206  for instance, are omitted from the illustration of  FIG.  2 C  for convenience in description. 
     In some implementations, device  200  may be configured to operate cameras  208   a,    208   b,    208   c,  and/or  208   d  in a rolling shutter configuration to obtain a sequence of image pixels. In the scenario of  FIG.  2 C  for example, a first row of sensing elements that includes sensing elements  228   a  and  228   g  may be configured to measure an amount of external light incident thereon during a first exposure time period. Device  200  may also include an analog to digital converter (not shown) that reads and converts the measurements by the first row of sensing elements (after the first exposure time period lapses) for transmission to a controller (e.g., controller  104 ) of device  200 . After a time delay from a start time of the first exposure time period (and optionally before the first exposure time period ends), device  200  may start exposing a second row of sensing elements that includes sensing elements  228   b  and  228   h  for a second exposure time period. Thus, in some examples, exposure time periods of multiple rows of sensing elements may partially overlap (e.g., the time delay between the start times of the first and second exposure time periods may be less than the first exposure time period, etc.). In this way, a camera in the rolling shutter configuration can stagger the start times of the exposure time periods to increase the image refresh rate (e.g., by simultaneously exposing multiple rows of sensing elements during the overlapping portions of their respective exposure time periods). 
     Continuing with the scenario, after the second exposure time period lapses, device  200  may then similarly measure and transmit the measurements by the second row of sensing elements to the controller. This process can then be repeated until all the rows of sensing elements (i.e., a complete image frame) are scanned. For example, after a start time of the second exposure time period (and optionally before the second exposure time period lapses), device  200  may begin exposing a third row of sensing elements (adjacent to the second row) to external light  270 , and so on. 
     Further, as noted above, device  200  may be configured to obtain the sequence of image pixels in an order that is similar to the order in which light pulses are emitted by LIDAR  206 . By doing so, for instance, more image pixels captured by cameras  208   a - d  may overlap (in both time and viewing direction) with LIDAR data (e.g., detected reflections of the emitted light pulses) than in an implementation where the sequence of image pixels is obtained in a different order. 
     Continuing with the scenario of  FIG.  2 C  for example, light beams  250   a,    250   b,    250   c  may correspond to the emitted light  250  shown in  FIG.  2 A  when LIDAR  206  is at a first pointing direction or orientation about axis  242 . In the scenario, the device  200  may be configured to scan the first (vertical) row of sensing elements (e.g., including elements  228   a  and  228   g ) before scanning sensing elements in the second (vertical) row (e.g., including elements  228   b  and  228   h ). By doing so, the image pixels captured using the first row of sensing elements may be more likely to be matched with detected reflections of light beams  250   a - 250   c  in terms of both time and viewing direction. In the scenario, LIDAR  206  may then rotate (e.g., counterclockwise) about axis  242  and emit light beams  252   a - 252   c.  Device  200  may then obtain a second row of image pixels using the second row of sensing elements (e.g., including sensing elements  228   b  and  228   h ), which may be more likely to be aligned (in both time and viewing direction) with detected reflections of light beams  252   a - 252   c,  and so on. 
     On the other hand, if device  200  instead obtained the sequence of image pixels in the order of a row that includes sensing elements  228   a - 228   f  (e.g., a horizontal row), followed by an adjacent (horizontal) row of sensing elements, etc., then the sequence of image pixels would be less likely to match the detected reflections of light beams  250   a - 250   c  and  252   a - 252   c.  By way of example, if image sensor  226  is operated at 60 Hz refresh rate (i.e., 60 images per second) using the horizontal (row by row) scanning order, then an image pixel associated with sensing element  228   g  in the obtained sequence may have a time delay of approximately 16 milliseconds compared to the emission time of light beam  250   c.  Other example refresh rates and/or time delays are possible. 
     In some implementations, device  200  may also be configured to obtain a row of image pixels in the sequence according to the order of emission of the light pulses/beams by LIDAR  206 . As a variation of the scenario above for example, if LIDAR  206  emits light beams  250   a,    250   b,    250   c  in that order, then device  200  may be configured to obtain the image pixel row associated with the first row of sensing elements in a similar order (e.g., beginning with sensing element  228   a  and ending with sensing element  228   g ). Whereas, for instance, if LIDAR  206  emits light beams  250   c,    250   b,    250   a  in that order, then device  200  may instead be configured to obtain the image pixel row in an opposite order (e.g., beginning with sensing element  228   g  and ending with sensing element  228   a ). 
     Further, in some implementations, device  200  may be configured to adjust a time delay between capturing subsequent image pixel rows in the sequence of image pixels based on a rate of rotation of LIDAR  206 . For example, if LIDAR  206  increases its rate of rotation (e.g., via actuator(s)  112 , etc.), then device  200  may reduce the time delay between obtaining the first row of image pixels associated with the first row of sensing elements (e.g., including sensing elements  228   a  and  228   g ) and obtaining the second row of image pixels associated with the second adjacent row of sensing elements. As noted above, for instance, the exposure start times associated with each row of sensing elements may depend on the order and time of obtaining the corresponding image pixels, and thus adjusting the time delay may improve the extent of matching image pixel capture times (and viewing directions) with corresponding LIDAR pulse emission times (and/or detections of corresponding reflections). 
       FIG.  2 D  illustrates a top view of device  200 . In the illustration of  FIG.  2 D , LIDAR  206  may have a first pointing direction that corresponds to an angular position of 0° about axis  242  (e.g., toward the bottom of the page). In this configuration for example, LIDAR  206  may scan a region of the surrounding environment that corresponds to a center of an image captured using camera  208   c  (best shown in  FIG.  2 B ), which includes lens  234 . 
       FIG.  2 E  illustrates another top view of device  200 . In the illustration of  FIG.  2 E , LIDAR  206  may have a second pointing direction that corresponds to an angular position of 180° about axis  242  (e.g., toward the top of the page). For instance, LIDAR  206  may have the second pointing direction of  FIG.  2 E  after LIDAR  206  is rotated from the first pointing direction of  FIG.  2 D  by half a complete rotation about axis  242 . Further, in this configuration for example, LIDAR  206  may scan a region of the environment that corresponds to a center of an image captured using camera  208   a  (best shown in  FIG.  2 B ), which includes lens  230 . 
     In some scenarios, as LIDAR  206  rotates about axis  242 , the time period in which FOVs of LIDAR  206  overlap the FOV of camera  208   a  may be less than the exposure time period (and readout time period) suitable for capturing an image using camera  208   a.    
     In one example scenario, where camera  208   a  is operated in a rolling shutter configuration (e.g., rows of sensing elements in camera  208   a  exposed according to different exposure start times), image sensor  226  of camera  208   a  may measure and output an image frame (i.e., pixel data from all the sensing elements of image sensor  226 ) over a period of 60 ms. Further, in the scenario, LIDAR  206  may be configured to rotate at a rotation rate of 10 Hz (i.e., one complete rotation about axis  242  every 100 ms). Thus, LIDAR  206  may scan a range of FOVs that overlap an FOV of camera  208   a  within a time period of 100 ms/4=25 ms (e.g., from t=37.5 ms to t=62.5 ms). To account for the difference between the scanning durations of the camera and the LIDAR, in some implementations, device  200  may be configured to synchronize LIDAR light pulses emitted by LIDAR  206  with image pixels captured by some but not all the image sensing elements in a camera. 
     For example, device  200  can be configured to trigger capturing an image by a particular camera such that a particular region of the image (e.g., vertical row(s) of image pixels at or near the center of the image, etc.) is exposed to external light when LIDAR  206  is pointing at a particular pointing direction aligned with the particular region of the image. 
     Continuing with the scenario above for instance (where LIDAR  206  rotates at a frequency of 10 Hz), at time t=0 ms, LIDAR  206  (as shown in  FIG.  2 D ) may have a first pointing direction (e.g., angular position about axis  242 =0°). Further, at time t=50 ms, LIDAR  206  (as shown in  FIG.  2 E ) may have a second pointing direction (e.g., angular position about axis  242 =180°). 
     In this scenario, device  200  may be configured to synchronize a center of the exposure time period of image sensor  226  (inside camera  208   a ) with the time (e.g., t=50 ms) at which the FOV of LIDAR  206  overlaps the center of the FOV of camera  208   a.  For example, where the exposure time period of image sensor  226  is 60 ms, then at time t=30 ms the center vertical rows of sensing elements in image sensor  226  may be exposed to external light. In this example, camera  208   a  may trigger an image capture at time t=50−30=20 ms to align (in both the time domain and space domain) exposure of vertical row(s) of sensing elements near the center of image sensor  226  with the LIDAR light pulses emitted when LIDAR  206  is scanning a FOV that corresponds to the center of the image (e.g., at t=50 ms). 
     With this arrangement, image pixels near the center of the image (e.g., captured using the vertical row including sensing element  228   c,  or the row including sensing element  228   d ) may be relatively more aligned (with respect to timing and viewing direction) with LIDAR light pulses that were emitted/detected when these image pixels were measured. On the other hand, image pixels captured using rows of sensing elements that are further from the center of the image sensor may be relatively misaligned (in time or viewing direction) with LIDAR light pulses that were emitted/detected when these image pixels were measured. By way of example, the FOVs of the rotating LIDAR may overlap the camera FOV of camera  208   a  between times t=37.5 ms and t=62.5 ms. In the scenario above however, camera  208   a  may begin exposing the row of sensing elements that include sensing element  228   a  (best shown in  FIG.  2 C ) at time t=20 ms (i.e., outside the range of times when the FOV of the LIDAR overlaps the FOV of the camera). 
     To mitigate this misalignment, in some examples, cameras  208   a,    208   b,    208   c,    208   d  can be configured to have partially overlapping FOVs. For example, camera  208   d  (best shown in  FIG.  2 B ) may be configured to have a FOV that partially overlaps the FOV of adjacent camera  208   a.  Further, the exposure time period associated with a center region of an image captured using camera  208   d  can be synchronized with the time (e.g., t=25 ms) at which LIDAR  206  is pointing toward a FOV associated with the center of the image captured using camera  208   d.  Thus, in these examples, device  200  (or other computer) can use the aligned image pixel data from camera  208   d  (e.g., image pixels near center of captured image) instead of the misaligned image pixel data captured using camera  208   a  (e.g., image pixels further from the center of the image) for mapping with the LIDAR data. 
       FIG.  3    illustrates a cross-section view of another rolling shutter camera arrangement  308  (e.g., camera ring), according to example embodiments. Camera ring  308  may be similar to camera ring  208  shown in  FIG.  2 B . As shown, for example, axis  342  may be an axis of rotation of a LIDAR similarly to axis  242 . Further, for example, image sensor  326  may be similar to image sensor  226  (and/or  246 ) and may include an array of sensing elements, exemplified by sensing elements  328   a - 328   e,  which may be similar to sensing elements  228   a - 228   f  For example, image sensor  326  may comprise a first row of sensing elements that includes sensing element  328   a  and one or more other sensing elements (not shown) in a linear arrangement (e.g., perpendicular to the page), and a second adjacent row of sensing elements that includes sensing element  328   b  and one or more other sensing elements (not shown) in a linear arrangement (e.g., perpendicular to the page). 
     Although not shown, camera ring  308  may also include one or more camera lenses (e.g., similar to camera lenses  230 ,  232 ,  234 ,  236 , etc.) that focus portions of external light incident on camera ring  308  toward respective sensing elements in the image sensor  326 . Additionally or alternatively, camera ring  308  may include one or more of the components shown in any of system  100  and/or device  200 . 
     As shown, camera ring  308  includes image sensor  326  that is disposed along a curved surface (e.g., circular surface) around axis  342 . In one example, image sensor  326  can be implemented on a flexible substrate (e.g., flexible PCB, etc.) that mounts an arrangement of sensing elements (including sensing elements  328   a - 328   e,  etc.). Thus, with this arrangement, each of the rows of sensing elements in image sensor  326  may be at a same given distance to the axis of rotation  342  (e.g., circular or cylindrical arrangement of sensing elements). In another example, image sensor  326  can be implemented as a plurality of physically separate rows of sensing elements that are arranged adjacent to one another around axis of rotation  342 . For instance, each physically separate row of sensing elements may be located at a same given distance to the axis of rotation as the other rows. Other examples are possible. Regardless of the implementation, in the configuration of camera ring  308 , the curved surface on which each row of sensing elements in image sensor  326  is mounted may improve the overlap (e.g., in terms of viewing direction) between the image pixels captured by the sensing elements and the light beams emitted by a LIDAR sensor that rotates about axis  342 . 
     For instance, as the LIDAR sensor rotates about axis  342 , the viewpoint of the LIDAR device (e.g., location of LIDAR lens) may move in a circular path. Thus, with this arrangement, the curved surface of image sensor  326  may resemble the circular path of emitted/detected LIDAR light pulses to improve the likelihood of matching image pixels collected by sensor  326  with LIDAR light pulses (that are detected along a similar curved path in the horizontal direction of the rotation of the LIDAR sensor). 
     III. Example Methods and Computer Readable Media 
       FIG.  4    is a flowchart of a method  400 , according to example embodiments. Method  400  presents an embodiment of a method that could be used with any of system  100 , device  200 , and/or camera ring  308 , for example. Method  400  may include one or more operations, functions, or actions as illustrated by one or more of blocks  402 - 404 . Although the blocks are illustrated in a sequential order, these blocks may in some instances be performed in parallel, and/or in a different order than those described herein. Also, the various blocks may be combined into fewer blocks, divided into additional blocks, and/or removed based upon the desired implementation. 
     In addition, for method  400  and other processes and methods disclosed herein, the flowchart shows functionality and operation of one possible implementation of present embodiments. In this regard, each block may represent a module, a segment, a portion of a manufacturing or operation process, or a portion of program code, which includes one or more instructions executable by a processor for implementing specific logical functions or steps in the process. The program code may be stored on any type of computer readable medium, for example, such as a storage device including a disk or hard drive. The computer readable medium may include a non-transitory computer readable medium, for example, such as computer-readable media that stores data for short periods of time like register memory, processor cache and Random Access Memory (RAM). The computer readable medium may also include non-transitory media, such as secondary or persistent long term storage, like read only memory (ROM), optical or magnetic disks, compact-disc read only memory (CD-ROM), for example. The computer readable media may also be any other volatile or non-volatile storage systems. The computer readable medium may be considered a computer readable storage medium, for example, or a tangible storage device. In addition, for method  400  and other processes and methods disclosed herein, each block in  FIG.  4    may represent circuitry that is wired to perform the specific logical functions in the process. 
     At block  402 , method  400  involves rotating a LIDAR sensor about an axis to scan an environment of the LIDAR sensor. For example, LIDAR sensor  206  may be mounted on rotating platform  210  that rotates about axis  242  to adjust a pointing direction of LIDAR sensor  206 . Further, while rotating, LIDAR sensor  206  may emit one or more light beams or pulses  250  toward a field-of-view of the environment defined by the pointing direction of LIDAR  206 , and then detect reflections (e.g., reflections  260 ) of the emitted light  250 . Further, an example system may process the detected reflections (e.g., via controller  104 ) to determine information (e.g., distance, texture, material, etc.) about one or more objects in the environment of LIDAR sensor  206 . 
     In some examples, an arrangement of the one or more light beams directed by the LIDAR sensor toward the environment may be based on rotation characteristics of the LIDAR sensor. Referring back to  FIG.  2 C  for example, the LIDAR sensor may emit light beams  250   a,    250   b,    250   c  when the LIDAR sensor is at a first pointing direction about axis  242 , and light beams  252   a,    252   b,    252   c  when the LIDAR sensor is at a second pointing direction about axis  242 . If the rotation characteristics of the LIDAR sensor are modified, then the arrangement of light beams  250   a,    250   b,    250   c,    252   a,    252   b,    252   c  may responsively change. For example, if the LIDAR sensor is rotated at a faster rate, then a distance between light beams  250   a,    250   b,    250   c  and light beams  252   a,    252   b,    252   c  may increase. As another example, if the LIDAR sensor is rotated in an opposite direction, then the directions of light beams  252   a,    252   b,    252   c  relative to the directions of light beams  250   a,    250   b,    250   c  may be different than shown in  FIG.  2 C . Other examples are possible. 
     At block  404 , method  400  involves obtaining, while rotating the LIDAR sensor, a sequence of image pixels using one or more cameras that detect external light originating from one or more external light sources. For example, the one or more cameras may together provide a plurality of adjacent rows of sensing elements, similarly to the arrangement of sensing elements in cameras  208   a,    208   b,    208   c,    208   d  (shown in  FIG.  2 B ). Further, one or more of the rows of sensing elements may be arranged according to a direction of the axis of rotation of the LIDAR sensor. Referring back to  FIG.  2 C  for example, a first row of sensing elements including elements  228   a  and  228   g  may be in a vertical arrangement similar to a vertical direction of axis  242 , and a second row of sensing elements including element  228   b  and  228   h  may also be in a vertical arrangement similar to the vertical direction of axis  242 , and so on. 
     Further, in some examples, a first image pixel row in the sequence may indicate a measurement of external light by the first row of sensing elements during a first exposure time period, and a second image pixel row in the sequence may indicate a measurement of the external light by a second row of sensing elements during a second exposure time period. For example, the one or more cameras could be operated in a rolling shutter configuration where image sensing elements (or rows) are sequentially measured (e.g., read out) to obtain the sequence of image pixels. Further, in line with the discussion above, the first exposure time period of the first row of sensing elements may begin before the second exposure time period of the second (subsequent) row of sensing elements begins. 
     For instance, a start time of the first exposure time period may be prior to a start time the second exposure time period. In this instance, a first line or row of image pixels in the sequence (obtained at block  404 ) may be based on data from the first row of sensing elements (exposed during the first exposure time period), and a second subsequent line or row of image pixels in the sequence may be based on data from the second row of sensing element (exposed during the second exposure time period). 
     With this arrangement, for instance, the first row of sensing elements may begin imaging a first region of the environment within a first FOV of the first row before the second row begins imaging a second (e.g., horizontally adjacent) region of the environment. Further, the LIDAR sensor can be rotated about the axis in a rotation direction that causes the LIDAR sensor to scan the first FOV imaged by the first row of sensing elements prior to scanning the second FOV imaged by the second row of sensing elements. 
     Accordingly, in some implementations, obtaining the sequence of image pixels (or image pixel rows) may comprise determining a timing configuration for operating the one or more cameras based on at least one or more rotation characteristics of the LIDAR sensor (and/or an arrangement of light beams directed by the LIDAR sensor toward the environment). In these implementations, method  400  may also involve operating the one or more cameras to obtain the sequence of image pixels at block  404  according to the determined timing configuration. 
     Referring back to  FIG.  2 C  for example, device  200  may choose the order of the image pixels in the sequence for a particular row of sensing elements based on the order in which corresponding light beams were emitted by the LIDAR sensor. For instance, where LIDAR  206  emits light beams  250   c,    250   b,    250   a  (in that order), then device  200  may obtain a first row of image pixels in the sequence beginning with a pixel associated with sensing element  228   g  and ending with a pixel associated with sensing element  228   a.  Whereas, if LIDAR  206  emits light beams  250   a,    250   b,    250   c  (in that order), then device  200  may obtain the first row of image pixels in the sequence beginning with a pixel associated with sensing element  228   a  and ending with a pixel associated with sensing element  228   g.  Accordingly, in some implementations, method  400  may involve operating an image sensor (e.g., of the one or more cameras) to obtain a sequence of image pixels that are measured in a particular order based on the arrangement (and/or order of emission) of the plurality of light beams emitted by the LIDAR sensor. 
     As another example, device  200  may select which row of sensing elements in sensor  226  to read for image pixels depending on the orientation or pointing direction of LIDAR  206 . For example, device  200  may trigger exposure of image sensor  226  (shown in  FIG.  2 B ) such that the center of the exposure time period of image sensor  226  is synchronized with the time at which the FOV of LIDAR  206  overlaps the center of the image (e.g., the time at which LIDAR  206  is at the orientation shown in  FIG.  2 E ). 
     Accordingly, in some implementations, method  400  involves obtaining the sequence of image pixels based on at least the angular direction (e.g., pointing direction, viewing direction, etc.) of the LIDAR sensor. Further, in some implementations, method  400  may involve determining a start time of a camera exposure time period of a particular camera of the one or more cameras based on a time associated with the LIDAR sensor being in a particular orientation about the axis of rotation. In some examples, the particular orientation may define a particular FOV of the LIDAR sensor that at least partially overlaps a region of the environment represented in a portion of the image captured using the particular camera (e.g., center region of the camera, etc.). 
     Referring back to  FIG.  2 B  for example, camera  208   a  may focus external light from a FOV of camera  208   a  toward image sensor  226 . Further, camera  208   a  may be operated in a rolling shutter mode where the camera begins exposing a first row of sensing elements that includes element  228   a,  then begins exposing a second row that includes element  228   b  after a time delay, and so on. The camera exposure time period of camera  208   a  may include respective exposure time periods of all the rows of sensing elements in image sensor  226 . Further, in this example, a system of method  400  may trigger capturing an image by camera  208   a  such that a center of the camera exposure time period corresponds to a given time when LIDAR  206  is pointing toward a center of the FOV of camera  208   a  (e.g., the given time when LIDAR  206  is at the orientation shown in  FIG.  2 E ). 
     In some implementations, method  400  involves determining a start time for the first exposure period (of the first row of sensing elements) based on one or more emission times of one or more light pulses (or beams) emitted by the LIDAR sensor, and operating the one or more cameras based on the determined start time, in line with the discussion above (e.g., select the appropriate row depending on the emission times of light beams  250   a - 250   c  and/or  252   a - 252   c  and the pointing direction of the LIDAR sensor when emitting the respective light beams). 
     In some implementations, method  400  involves determining an order of respective exposure start times of adjacent rows of sensing elements to the external light based on at least a direction of the rotation of the LIDAR sensor, and operating the one or more cameras based on the determined order. Referring back to  FIG.  2 C  for example, after obtaining the image pixel row associated with elements  228   b  and  228   h,  device  200  may next obtain an image pixel row associated with sensing elements  228   a  and  228   g  if LIDAR  206  is rotating in a clockwise direction. On the other hand, if LIDAR  207  is rotating in a counterclockwise direction, then device  200  may instead obtain an image pixel row associated with the row of sensing elements that includes element  228   c.    
     In some implementations, method  400  involves determining a time delay based on a rate of rotation of the LIDAR sensor, obtaining the first image pixel row using the first row of sensing elements, and obtaining the second image pixel row using the second row of sensing elements after passage of the determined time delay from obtaining the first image pixel row. Referring back to  FIG.  2 C  for example, device  200  may increase the time delay between obtaining the first image pixel row (including elements  228   a  and  228   g ) and the second image pixel row (including elements  228   b  and  228   h ) if the rate of rotation of LIDAR  206  decreases, or may decrease the time delay if the rate of rotation of LIDAR device  206  increases. 
     As a variation of the example above, device  200  may begin the first exposure time period of the first row of sensing elements (including elements  228   a  and  228   g ) at a first start time, and then begin the second exposure time period of the second row of sensing elements (including elements  228   b  and  228   h ) at a second start time (i.e., after passage of the determined time delay). Thus, in some implementations, method  400  involves determining a time delay between a start time of the first exposure time period and a start time of the second exposure time period based on rotation characteristics of the LIDAR sensor (e.g., rate of rotation, etc.), and operating the one or more cameras according to the determined time delay. 
     In some implementations, method  400  involves controlling respective camera exposure time periods of the one or more cameras based on the pointing direction (and/or rotation characteristics) of the LIDAR sensor. In other implementations, method  400  involves modifying one or more rotation characteristics of the LIDAR sensor based on the respective camera exposure time periods of the one or more cameras. Referring back to  FIG.  2 B  for example, cameras  208   a,    208   b,    208   c,    208   d  can be operated to capture images using their respective image sensors in a particular rolling shutter configuration that defines the order of image pixels in the sequence of image pixels obtained at block  404 . In this example, a system of method  400  can operate the LIDAR sensor to emit light beams in an order and arrangement that matches the order and arrangement of the image pixels measured by the cameras. 
     Referring back to  FIG.  2 C  for example, the system of method  400  can control the order of emitted light beams  250   a,    250   b,    250   c  to match the order in which a first row of sensing elements including elements  228   a  and  228   g  are measured (e.g., top to bottom, or bottom to top). As another example, the system can adjust the frequency of rotation of LIDAR  206  according to the time delay between measurement of image pixels associated with the first row and measurement of image pixels associated with a second row that includes sensing elements  228   b  and  228   h.  As yet another example, the system may adjust the direction of the rotation of LIDAR  206  to match the direction of the “slow axis” of camera  208   a.  Other examples are possible. 
     In some implementations, method  400  involves associating data from the LIDAR sensor with one or more image pixels in the sequence of image pixels (or image pixel rows) based on at least the timing configuration. For example, an example system may keep track of times in which individual image pixels were captured as well as the times in which reflected LIDAR pulses are detected. Further, the example system may then map image pixels that were captured with a threshold period of time to a corresponding LIDAR pulse detection. 
     Alternatively or additionally, in some implementations, method  400  involves associating data from the LIDAR sensor with one or more image pixels in the sequence of image pixels based on at least pointing directions of the LIDAR sensor and viewing directions of the sensing elements in the array. Referring back to  FIG.  2 C  for example, data based on LIDAR  206  detecting reflections of light pulses  250   a,    250   b,    250   c  when LIDAR  206  is in a first pointing direction can be matched with image pixels collected using one or more of the image sensing elements in the row that includes sensing elements  228   a  and  228   g  (e.g., which correspond to image sensing elements that have a same or similar viewing direction as the LIDAR pointing direction at which light pulses  250   a,    250   b,    250   c  were emitted). Accordingly, in some implementations, method  400  involves associating a given image pixel row in the sequence with given data collected by the LIDAR sensor based on at least a comparison of a field-of-view (FOV) of a first row of sensing elements to a FOV of the LIDAR sensor when the given data was collected by the LIDAR sensor. 
     In some implementations, method  400  involves determining a three-dimensional (3D) representation of the environment based on data from the LIDAR sensor and the sequence of image pixels (or image pixel rows). For example, an example system may combine LIDAR-based information (e.g., distances to one or more objects in the environment, etc.) with camera-based information (e.g., color, etc.) to generate the 3D representation. Other types of representations (e.g., 2D image, image with tags, enhanced image that indicates shading or texture information, etc.) based on a combination of LIDAR and image data are possible. Thus, in some implementations, method  400  involves determining a representation of the environment based on color information indicated by the sequence of image pixels (or image pixel rows) and information (e.g., distance, depth, texture, reflectivity, absorbance, reflective light pulse length, etc.) indicated by the LIDAR sensor. 
     In a first example, a system of method  400  may determine depth information for image pixels in the sequence based on data from the LIDAR sensor. For instance, the system can assign a depth value for image pixels in an image. Additionally, for instance, the system can generate (e.g., via display  140 ) a 3D object data model (e.g., a 3D rendering) of one or more objects in the environment (e.g., colored 3D model that indicates 3D features in the environment, etc.). In another instance, an image processing system can identify and distinguish between multiple objects in the image by comparing depth information (indicated by the associated LIDAR data) of the respective objects. Other applications are possible. Thus, in some implementations method  400  involves mapping LIDAR data points collected using the LIDAR sensor to image pixels collected using the one or more cameras. For instance, the LIDAR data can be mapped to a coordinate system of an image output by an image sensor or camera. 
     In a second example, a system of method  400  may assign colors (based on data from the one or more cameras) to individual points in a LIDAR point cloud. The example system can then generate (e.g., via display  140 ) a 3D rendering of the scanned environment that indicate distances to features in the environment along with color (e.g., colored point cloud, etc.) information indicated by the image sensor(s) of the one or more cameras. Thus, in some implementations, method  400  involves mapping image pixels from the sequence to corresponding LIDAR data points obtained using the LIDAR sensor. For instance, the image pixel data can be mapped to a coordinate system of the LIDAR data output by the LIDAR sensor. 
     In a third example, a system of method  400  may identify and assign particular LIDAR data point(s) and particular image pixel(s) to particular location(s) in another space (e.g., 3D space, etc.). For instance, the system can display and update (e.g., via display  140 ) a representation of the environment using data from the LIDAR sensor, the one or more cameras, and/or other sensors (e.g., RADARs, SONARs, etc.) accessible to the system. Accordingly, in some implementations, method  400  involves mapping image pixels from the sequence and LIDAR data points from the LIDAR sensor to a given 3D space. Thus, for instance, the LIDAR data and image pixel data can be mapped to a coordinate system other than the coordinate systems defined by LIDAR and the camera(s). 
     In some implementations, method  400  involves mounting the one or more cameras in a ring arrangement. Referring back to  FIG.  2 B  for example, cameras  208   a,    208   b,    208   c,    208   d  may be mounted in a ring or circular arrangement (e.g., around axis  242 ) to image respective portions of the environment of camera ring  208 . 
     In a first implementation, mounting the one or more cameras may be based on one or more characteristics of a given camera of the one or more in the ring arrangement. For example, the given camera can be individually tested to ensure that an extent of a given FOV imaged by the given camera is within a threshold range from a target FOV (e.g., 90 degrees, 45 degrees, etc.). To facilitate this, for instance, an example system may compare images obtained by the given camera with a stored image of the target FOV, etc. Thus, in some examples, method  400  may also involve adjusting the one or more cameras based on the determined one or more characteristics. 
     In a second implementation, mounting the one or more cameras may be based on one or more characteristics associated with the relative mounting positions of the one or more cameras in the ring arrangement. In one example, a system of method  400  may determine a distance (from a device that includes the cameras) at which FOVs imaged by two adjacent cameras intersect or overlap. Objects in the environment within the determined distance may be undetected (e.g., “blind spot”) by the system using the adjacent cameras. Further, for instance, the determination of the distance may be based on images captured using the cameras, sensor data captured by other sensors (e.g., LIDAR data, etc.) that image the same region of the environment, etc. In another example, the system may be configured to determine the relative rotational orientations of the one or more cameras. For example, respective offsets in roll, pitch, etc., directions of the cameras can be determined (e.g., based on images captured by the cameras, etc.) and/or compared to one another. Thus, in some examples, method  400  may also involve adjusting the relative mounting positions of the one or more cameras based on the determined one or more characteristics. 
     In a third implementation, mounting the one or more cameras may involve determining one or more characteristics associated with one or more other sensors that scan the environment imaged by the one or more cameras in the ring arrangement. For example, the system may compare the FOVs of the one or more cameras with a FOV of the LIDAR, a FOV of a RADAR, or a FOV of any other sensor in the system. Further, in some instances, the system may mount the one or more cameras in the ring arrangement at particular mounting positions to achieve a target overlap between the FOVs of the one or more cameras and the FOVs of the one or more sensors. Thus, in some examples, method  400  may also involve adjusting the mounting positions of the one or more cameras based on the determined one or more characteristics associated with the one or more sensors. 
     In a fourth implementation, mounting the one or more cameras may involve determining whether images captured using the one or more cameras in the ring arrangement are indicative of one or more objects in the environment. For example, the system may determine if the FOVs of the camera(s) are suitable for detecting objects in particular positions relative to the system (e.g., traffic lights under various lighting conditions, pedestrians, construction cones, etc.). Further, in some examples, method  400  may also involve adjusting the mounting positions or other characteristic (e.g., exposure time, image brightness, etc.) of the one or more cameras based on the determination of whether the captured images are indicative of the one or more objects. 
     IV. Conclusion 
     The particular arrangements shown in the Figures should not be viewed as limiting. It should be understood that other implementations may include more or less of each element shown in a given Figure. Further, some of the illustrated elements may be combined or omitted. Yet further, an exemplary implementation may include elements that are not illustrated in the Figures. Additionally, while various aspects and implementations have been disclosed herein, other aspects and implementations will be apparent to those skilled in the art. The various aspects and implementations disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. Other implementations may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein. It will be readily understood that the aspects of the present disclosure, as generally described herein, and illustrated in the figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations.