Abstract:
Within machine vision, object movement is often estimated by applying image evaluation techniques to visible light images, utilizing techniques such as perspective and parallax. However, the precision of such techniques may be limited due to visual distortions in the images, such as glare and shadows. Instead, lidar data may be available (e.g., for object avoidance in automated navigation), and may serve as a high-precision data source for such determinations. Respective lidar points of a lidar point cloud may be mapped to voxels of a three-dimensional voxel space, and voxel clusters may be identified as objects. The movement of the lidar points may be classified over time, and the respective objects may be classified as moving or stationary based on the classification of the lidar points associated with the object. This classification may yield precise results, because voxels in three-dimensional voxel space present clearly differentiable statuses when evaluated over time.

Description:
BACKGROUND 
     Within the field of machine vision, many scenarios involve the detection of movement of various objects, such as object motion tracking and speed estimation techniques and devices. For example, a sequence of images of a scene captured over a brief period of time may be evaluated to identify a particular object that is visible in several sequential images, and based on various geometric properties of the scene, the movement of the object through the scene over the captured time period may be estimated. Some such scenarios may involve a realtime or near-realtime evaluation of such objects, while other scenarios may involve a retrospective evaluation of previously captured images; e.g., a vehicle moving down a street may capture a sequence of images of the scene to be stitched together to form a panoramic or dynamic view of the scene, and the images may later be post-processed to remove obstructions of the view by transient objects present during the capturing. 
     SUMMARY 
     This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key factors or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     While objects may be identified, estimated as moving or stationary, and removed from a sequence of natural-light images, the precision of such estimation that is achievable through only analysis of visual image may be limited. However, the analysis may be supplemented with ranging information about such objects gathered by lidar, which is often included in vehicles for object detection, avoidance, and navigation. The availability and use of lidar may provide a high-precision source of data that may be particularly revealing in an evaluation of whether such objects are moving or stationary, as each type of object may present a distinctly different signature of lidar data. 
     In view of these observations, presented herein are techniques for detecting movement of objects depicted in a lidar point cloud. These techniques involve mapping respective lidar points in the lidar point cloud to a voxel in a three-dimensional voxel space, and identifying at least one object represented by a voxel cluster of voxels sharing an adjacency within the three-dimensional voxel space. These techniques also involve, for the respective lidar points in the lidar point cloud, associating the lidar point with a selected object, and classifying the movement of the lidar point according to the selected object. Finally, these techniques involve, for the respective objects, classifying the movement of the object according to the movement of the respective lidar points associated with the object. By achieving the detection of movement of respective objects depicted in the lidar point cloud in this manner, the techniques presented herein may enable a high-precision classification of moving vs. stationary objects in the visual space, where such classification may be usable for a variety of further processing techniques (e.g., focusing one or more images on the object; estimating a position, orientation, velocity, and/or acceleration of the object; and removing the object from images depicting the area represented by the lidar point cloud) in accordance with the techniques presented herein. 
     To the accomplishment of the foregoing and related ends, the following description and annexed drawings set forth certain illustrative aspects and implementations. These are indicative of but a few of the various ways in which one or more aspects may be employed. Other aspects, advantages, and novel features of the disclosure will become apparent from the following detailed description when considered in conjunction with the annexed drawings. 
    
    
     
       DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is an illustration of an exemplary scenario featuring a vehicle moving within an environment while capturing images of the environment and other objects present in the environment. 
         FIG. 2  is an illustration of an exemplary scenario featuring a capturing of a lidar point cloud of an environment around a vehicle and depicting the other objects present within the environment. 
         FIG. 3  is an illustration of an exemplary scenario featuring an evaluation of a lidar point cloud over time to classify identified objects as stationary or moving in accordance with the techniques presented herein. 
         FIG. 4  is a flow diagram of an exemplary method of evaluating a lidar point cloud over time to classify identified objects as stationary or moving in accordance with the techniques presented herein. 
         FIG. 5  is a component block diagram of an exemplary system configured to evaluate a lidar point cloud over time to classify identified objects as stationary or moving in accordance with the techniques presented herein. 
         FIG. 6  is an illustration of an exemplary computer-readable medium comprising processor-executable instructions configured to embody one or more of the provisions set forth herein. 
         FIG. 7  is an illustration of an exemplary scenario featuring a repartitioning of a lidar point cloud following a classification of objects in order to distinguish nearby objects in accordance with the techniques presented herein. 
         FIG. 8  illustrates an exemplary computing environment wherein one or more of the provisions set forth herein may be implemented. 
     
    
    
     DETAILED DESCRIPTION 
     The claimed subject matter is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. It may be evident, however, that the claimed subject matter may be practiced without these specific details. In other instances, structures and devices are shown in block diagram form in order to facilitate describing the claimed subject matter. 
     A. Introduction 
     Within the field of machine vision, many scenarios involve an automated evaluation of images of an environment to detect the objects present in the environment and depicted in the images, and, more particularly, to identify the position, size, orientation, velocity, and/or acceleration of the objects. As a first example, the evaluation may involve vehicles in a transit environment, including automobiles, bicycles, and pedestrians in a roadway as well as signs, trees, and buildings, in order to facilitate obstacle avoidance. As a second example, a physical object tracking system may evaluate the motion of an object within an environment in order to interact with it (e.g., to catch a ball or other thrown object). As a third example, a human actor present in a motion-capture environment may be recorded while performing various actions in order to render animated personalities with human-like movement. In various scenarios, the analysis may be performed in realtime or near-realtime (e.g., to facilitate a device or individual in interacting with the other present objects), while in other scenarios, the analysis may be performed retrospectively (e.g., to identify the movement of objects that were present at the time of the capturing). These and other scenarios often involve the capturing and evaluation of a set of visible light images, e.g., with a still or motion camera, and the application of visual processing techniques to human-viewable images. For example, machine vision techniques may attempt to evaluate, from the contents of the image, the type, color, size, shape, orientation, position, speed, and acceleration of an object based on visual cues such as shadowing from light sources, perspective, relative sizes, and parallax effects. 
       FIG. 1  presents an illustration of an exemplary scenario featuring a vehicle  102  operating in an environment  100  (e.g., with a particular velocity vector  104  while operating a camera  106  to capture a sequence of images of the environment  100 . In this exemplary scenario, other vehicles  102  are also present in the environment  100 , and may involve both vehicles  102  having a velocity vector  104  and stationary vehicles  108 , such as parked cars. The environment  100  may also include other types of moving objects, such as individuals  110 , as well as various stationary objects, such as signs  112  and buildings  114 . In this environment, an operator of the vehicle  102  may endeavor to identify the velocity vectors  104  of respective vehicles  102 , as well as of individuals  110 , and to distinguish between moving vehicles  102  and stationary vehicles  108 , as well as other types of objects that are moving or stationary. The results of this analysis, if performed in near-realtime, may assist in the navigation of the vehicle  102  (such as matching speed with other nearby vehicles  102  and applying brakes and steering to avoid sudden velocity changes). Additionally, the results of this analysis may be retrospectively useful, e.g., for removing the depicted vehicles  102  and other objects from the images captured by the camera  106  in order to generate an unobstructed view of the environment  100  and/or to generate a more accurate three-dimensional model of the moving objects for various applications, including sharpened visualization, further classification of the object (e.g., identifying the make and model of a moving vehicle), and movement tracking. 
     However, in these scenarios, the achievable precision in the identification of the movement of the objects from an inspection of visual images may be limited. For example, techniques such as perspective and parallax may provide only general estimates, particularly for objects that are distant from the camera lens, and/or may be distorted by visual artifacts, such as glare and shadows. As a result, such evaluative techniques may produce estimates with low precision and/or a high degree of error, and may be inadequate for particular uses. 
     B. Presented Techniques 
     Many scenarios involving the evaluation of object movement may be achieved through devices (such as vehicles  102 ) that also have access to data from a laser imaging (“lidar”) capturing device, which may emit a set of focused, low-power beams of light of a specified wavelength, and may detect and record the reflection of such wavelengths of light from various objects. The detected lidar data may be used to generate a lidar point cloud, representing the lidar points of light reflected from the object and returning to the detector, thus indicating specific points of the objects present in the environment  100 . By capturing and evaluating lidar data over time, such a device may build up a representation of the relative positions of objects around the lidar detector (e.g., the locations of other vehicles  102  with respect to the vehicle  102  operating the camera  106 ). 
       FIG. 2  presents an illustration of an exemplary scenario  200  featuring a capturing of objects (e.g., vehicles  102 ) using a lidar point cloud. In this exemplary scenario  200 , a first vehicle  102  is positioned behind a moving vehicle  102  having a velocity vector  104 , and a stationary vehicle  108  having no detectable motion. The first vehicle  102  may comprise a lidar emitter  202  that emits a lidar signal  204  ahead of the first vehicle  102 . The lidar reflection  206  of the lidar signal  204  may be detected by a lidar detector  208 , and captured as a sequence of lidar point clouds  210  representing, at respective time points  212 , the lidar points  214  detected by the lidar detector  208  within the environment  100 . In particular, the detected lidar points  214  may cluster around particular objects (such as vehicles  102 ), which may enable the lidar detector  208  to identify the presence, size, and/or range of the objects at respective time points  212 . Additionally, by comparing the ranges of the vehicles  102  or other objects over time, the lidar detector  208  may determine an approximate velocity of the objects. For example, when comparing the lidar point clouds  210  over time, the lidar points  214  representing the moving vehicle  102  and the lidar points  214  representing the stationary vehicle  108  may move with respect to each other and the first vehicle  102 . However, if the vehicle  102  carrying the lidar detector  208  is also moving, the approximate velocities of the vehicles  102  or other objects represented by the lidar points  214  in the lidar point cloud  210  may be distorted; e.g., stationary vehicles  108  may appear to be moving, while moving vehicles  102  that are moving at an approximately equivalent velocity and direction as the vehicle  102  carrying the lidar detector  208  may appear as stationary vehicles  108 . Such complications may be come exacerbated if the objects are detected as moving in three-dimensional space as well as over time, and/or if the orientation of the vehicle  102  carrying the lidar detector  208  also changes (e.g., accelerating, decelerating, and/or turning). Even determining whether respective objects (such as vehicles  102 ) are moving or stationary may become difficult in view of these factors. 
     In order to classify respective objects (such as vehicles  102 ) as moving or stationary, and optionally in order to identify other properties such as position and velocity, techniques may be utilized to translate the lidar points  214  of the respective lidar point clouds  210  to three-dimensional space.  FIG. 3  presents an illustration of an exemplary scenario  300  featuring a translation of a set of lidar point clouds  210  to classify the objects depicted therein. In this exemplary scenario  300 , for respective lidar point clouds  210 , the lidar points  214  are mapped  302  to a voxel  306  in a three-dimensional voxel space  304 . Next, the voxels  306  of the three-dimensional voxel space  304  may be evaluated to detect one or more voxel clusters of voxels  306  (e.g., voxels  306  that are occupied by one or more lidar points  214  in the lidar point cloud  210 , and that share an adjacency with other occupied voxels  306  of the three-dimensional voxel space  304 , such as within a specified number of voxels  306  of another occupied voxel  306 ), resulting in the identification  308  of one or more objects  312  within an object space  310  corresponding to the three-dimensional voxel space  304 . Next, for the respective lidar points  214  in the lidar point cloud  210 , the lidar point  214  may be associated with a selected object  312 . The movement of the lidar points  214  may then be classified according to the selected object  312  (e.g., the objects may be identified as moving or stationary with the object  312  in the three-dimensional voxel space  304 ). According to the classified movements of the lidar points  214  associated with the object  312  (e.g., added for the object spaces  310  at respective time points  212 ), a projection  314  of the lidar points  214  and an evaluation of the movements of the lidar points  214  associated with respective objects  312 , the movement of the respective objects  312  may be classified. For example, and as depicted in the projection  314  of  FIG. 3 , the lidar points  214  associated with the first object  312 , after projection in view of the three-dimensional voxel space  304 , appear to be moving with respect to the lidar detector  208 , and may result in a classification  316  of the object  312  as a moving object; while the lidar points  214  associated with the second object  312 , after projection in view of the three-dimensional voxel space  304 , appear to be stationary after adjusting for the movement of the lidar detector  208 , and may result in a classification  316  of the object  312  as a stationary object. In this manner, the techniques illustrated in the exemplary scenario  300  of  FIG. 3  enable the classification  316  of the objects  312  identified within the environment  100  depicted by the lidar point clouds  210  into stationary objects and moving objects in accordance with the techniques presented herein. 
     C. Exemplary Embodiments 
       FIG. 4  presents a first exemplary embodiment of the techniques presented herein, illustrated as an exemplary method  400  of detecting movement of objects  312  depicted in a lidar point cloud  210 . The exemplary method  400  may be implemented, e.g., as a set of instructions stored in a memory device of the device, such as a memory circuit, a platter of a hard disk drive, a solid-state storage device, or a magnetic or optical disc, and organized such that, when executed on a processor of the device, cause the device to operate according to the techniques presented herein. The exemplary method  400  begins at  402  and involves executing  404  the instructions on a processor of the device. Specifically, these instructions may be configured to map  406  respective lidar points  214 . Having achieved the identification and movement classification of the objects  312  presented in the environment  100  from the lidar point clouds  210  at respective time points  212 , the exemplary method  400  applies the techniques presented herein to the classification of the movement of the objects  312 , and so ends at  418 . 
       FIG. 5  presents a second exemplary embodiment of the techniques presented herein, illustrated as an exemplary system  506  configured to classify the movement of objects  312  depicted in a lidar point cloud  210  of an environment  100 . The exemplary system  506  may be implemented, e.g., as instructions stored in a memory component of the device  502  and configured to, when executed on the processor  504  of the device  502 , cause the device  502  to operate according to the techniques presented herein. The exemplary system  506  includes an object identifier  508  that is configured to map  302  respective lidar points  214  in the lidar point cloud  210  to a voxel  306  in a three-dimensional voxel space  304 , and identify  308  at least one object  312  represented by a voxel cluster of voxels  306  sharing an adjacency within the three-dimensional voxel space  304 . The exemplary system  506  also comprises an object movement classifier  510 , which is configured to, for the respective lidar points  214  in the lidar point cloud  210 , associate the lidar point  214  with a selected object  312 , classify the movement of the lidar point  214  according to the selected object  312 ; and, for the respective objects  312 , classify the movement of the object  312  according to the movement of the respective lidar points  214  associated with the object  312 . The interoperation of these components of the exemplary system  506  may enable the device  502  to achieve the classification  316  of the objects  312  represented by the lidar points  214  in the lidar point clouds  210  in accordance with the techniques presented herein. 
     Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to apply the techniques presented herein. Such computer-readable media may include, e.g., computer-readable storage media involving a tangible device, such as a memory semiconductor (e.g., a semiconductor utilizing static random access memory (SRAM), dynamic random access memory (DRAM), and/or synchronous dynamic random access memory (SDRAM) technologies), a platter of a hard disk drive, a flash memory device, or a magnetic or optical disc (such as a CD-R, DVD-R, or floppy disc), encoding a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein. Such computer-readable media may also include (as a class of technologies that are distinct from computer-readable storage media) various types of communications media, such as a signal that may be propagated through various physical phenomena (e.g., an electromagnetic signal, a sound wave signal, or an optical signal) and in various wired scenarios (e.g., via an Ethernet or fiber optic cable) and/or wireless scenarios (e.g., a wireless local area network (WLAN) such as WiFi, a personal area network (PAN) such as Bluetooth, or a cellular or radio network), and which encodes a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein. 
     An exemplary computer-readable medium that may be devised in these ways is illustrated in  FIG. 6 , wherein the implementation  600  comprises a computer-readable medium  602  (e.g., a CD-R, DVD-R, or a platter of a hard disk drive), on which is encoded computer-readable data  604 . This computer-readable data  604  in turn comprises a set of computer instructions  606  configured to operate according to the principles set forth herein. In one such embodiment, the processor-executable instructions  606  may be configured to perform a method  608  of classifying the movement of objects  312  according to a lidar point cloud  210 , such as the exemplary method  400  of  FIG. 4 . In another such embodiment, the processor-executable instructions  606  may be configured to implement a system for classifying the movement of objects  312  according to a lidar point cloud  210 , such as the exemplary system  506  of  FIG. 5 . Some embodiments of this computer-readable medium may comprise a computer-readable storage medium (e.g., a hard disk drive, an optical disc, or a flash memory device) that is configured to store processor-executable instructions configured in this manner. Many such computer-readable media may be devised by those of ordinary skill in the art that are configured to operate in accordance with the techniques presented herein. 
     D. Variations 
     The techniques discussed herein may be devised with variations in many aspects, and some variations may present additional advantages and/or reduce disadvantages with respect to other variations of these and other techniques. Moreover, some variations may be implemented in combination, and some combinations may feature additional advantages and/or reduced disadvantages through synergistic cooperation. The variations may be incorporated in various embodiments (e.g., the exemplary method  400  of  FIG. 4  and the exemplary system  506  of  FIG. 5 ) to confer individual and/or synergistic advantages upon such embodiments. 
     D1. Scenarios 
     A first aspect that may vary among embodiments of these techniques relates to the scenarios wherein such techniques may be utilized. 
     As a first variation of this first aspect, the techniques presented herein may be utilized to evaluate many types of objects, including vehicles  102  traveling in an environment  100 , such as automobiles and bicycles traveling on a roadway or airplanes traveling in an airspace; individuals moving in an area, such as a motion-capture environment  100 ; and projectiles moving in a space, such as ballistics. 
     As a second variation of this first aspect, the techniques presented herein may be utilized with many types of lidar signals  204 , including visible, near-infrared, or infrared, near-ultraviolet, or ultraviolet light. Various wavelengths of lidar signals  204  may present various properties that may be advantageous in different scenarios, such as passage through various media (e.g., water or air of varying humidity), sensitivity to various forms of interference, and achievable resolution. 
     As a third variation of this first aspect, the techniques presented herein may be utilized with various types of lidar emitters  202  and/or lidar detectors  208 , such as various types of lasers and photometric detectors. Additionally, such equipment may be utilized in the performance of other techniques (e.g., lidar equipment provided for range detection in vehicle navigation systems may also be suitable for the classification of moving and stationary objects), and may be applied to both sets of techniques concurrently or in sequence. 
     As a fourth variation of this first aspect, the classification  316  of the objects  312  according to the techniques presented herein may be usable in many ways. As a first example, the classification  316  of objects  312  as moving or stationary may be usable to identify the regions of corresponding images where the objects  312  are present. Object-based image evaluation techniques may therefore be focused on the specific areas of the images, e.g., in order to perform an automated evaluation and/or identification of the objects  312 ; in order to identify the portions of the images to be redacted (e.g., in furtherance of the privacy of individuals associated with the objects  312  in images to be made available to the public); and/or in order to compensate for the presence of the objects  312  in the images (e.g., removing objects classified as moving objects in order to avoid obscured areas of the images while reconstructing a three-dimensional model of the environment  100 ). As a second example, the classification  316  of objects  312  may facilitate object recognition, e.g., classifying the respective objects  312  as an object type according to the movement of the object  312 . For example, the device may comprise an object type classifier that is trained to select object type classifications of objects  312 , and the device may invoke the object type classifier to classify an object  312  into an object type in view of the classification  316  of the movement of the object  312 . Those of ordinary skill in the art may devise a broad variety of such scenarios and/or uses for the classification  316  of objects  312  according to the techniques presented herein. 
     D2. Voxel Mapping and Clustering 
     A second aspect that may vary among embodiments of the techniques presented herein relates to the mapping  302  of lidar points  214  of a lidar point cloud  210  to the voxels  306  of a three-dimensional voxel space  304 . 
     As a first variation of this first aspect, the mapping  302  may involve a three-dimensional voxel space  304  of various sizes. For example, the voxels  306  may be uniformly spaced across the three-dimensional voxel space  304 , or may vary in size and/or density. In an embodiment, in order to compensate for potential dispersal of the laser over distance, the voxels  306  further from the lidar detector  208  in the three-dimensional voxel space  304  may have a larger size than voxels  306  that are closer to the lidar detector  208 . 
     As a second variation of this second aspect, the mapping  302  may involve a comparison of an adjacency threshold between a selected voxel  306  and other voxels  306  of the three-dimensional voxel space  304 . For example, it may be determined that if a first voxel  306  that is mapped to at least one lidar point  214  is within an adjacency threshold of three voxels of a second voxel  306  that is mapped to at least one lidar point  214 , then the voxels  306  are determined to share an adjacency, and therefore are part of the same voxel cluster. As a further variation, the axes within the three-dimensional voxel space  304  may have different adjacency thresholds; e.g., a first axis within the three-dimensional voxel space  304  may have a first adjacency threshold, while a second axis within the three-dimensional voxel space  304  may have a second adjacency threshold that is different from the first adjacency threshold. In one such variation, the axis parallel to the view of the lidar detector  208  within the three-dimensional voxel space  304  may be selected as having a significantly larger adjacency threshold, since voxels  306  that are connected along this axis may be difficult to detect with a surface-based lidar reflection  206  (e.g., a first voxel  306  that is close to the lidar detector  208  may be connected to a second voxel  306  that is distant from the lidar detector  208  through a connecting piece of the object  312  oriented parallel to the axis of view, and thus not detected by reflective lidar). 
     As a third variation of this second aspect, the mapping  302  may determine that two or more voxel clusters are connected. For example, nearby voxel clusters may be connected by a portion of the object  312  that is not highly reflective, thus resulting in few lidar points  214 . The lidar detector  208  may conclude that if the voxel clusters are nearby and appear to move together, then the voxel clusters are likely connected and thus represent the same object  312 . Determining connectedness may be achieved, e.g., by selecting a first voxel cluster, performing a nearest neighbor search (e.g., a breadth-first search) of the three-dimensional voxel space  304 , and finding a second voxel cluster that appears to be connected to the first voxel cluster. These and other variations may facilitate the mapping of lidar points  214  to voxels  306  in the three-dimensional voxel space  304  in accordance with the techniques presented herein. 
     D3. Classifying Lidar Point Movement 
     A third aspect that may vary among embodiments of the techniques presented herein relates to the classification of the movement of the lidar points  214 . 
     As a first variation of this third aspect, the classification may be performed by classifying the movement of the respective voxels  306  within the three-dimensional voxel space  304 . For example, for respective voxels  306 , a measurement may be performed of an occupancy duration of the voxel over time by at least one lidar point  214  of the lidar point cloud  210 . If the occupancy duration of the voxel  306  is determined to be within an occupancy time variance threshold (e.g., if the voxel  306  is occupied by at least one lidar point  214  for more than a threshold duration), then the voxel  306  may be classified as stationary, as well as the lidar points  214  mapped to the voxel  306 . Conversely, lidar points  214  that are not mapped to a voxel  306  that is classified as stationary may be classified as moving. As one such variation, the occupancy duration may be measured and compared as a standard deviation of the occupancy duration of respective voxels  306  of the three-dimensional space  304 , and voxels  306  presenting an occupied status for more than one standard deviation may be classified as stationary. This variation may be advantageous, e.g., for reducing the effects of small variations in the positions of the lidar points  214  over time, such as rotation, shape changes, or detector jitter, that do not translate to motion. 
     As a second variation of this third aspect, rather than performing the classification of movement of lidar points  214  and/or objects  312  according to predefined calculations, the classification may be performed by a movement classifier that has been trained to select a classification  316  of movement of the lidar points  214  and/or objects  312 , such as an artificial neural network or genetic algorithm. Many such variations may be devised for classifying the movement of lidar points  214  and/or objects  312  in accordance with the techniques presented herein. 
     D4. Further Processing 
     A fourth aspect that may vary among embodiments of the techniques presented herein relates to additional processing that may be applied during the classification  316  of the movement of the objects  312 . 
     As a first variation of this fourth aspect, after classifying the movement of respective objects  312 , the device may map the lidar points  214  of the lidar point cloud  210  to the respective objects  312 . That is, after achieving the classification  316  of the objects  312  based on the aggregated, collective classification of the lidar points  214 , the individual lidar points  214  may then be remapped to the objects  312  in order to verify that substantially all of the lidar points  214  of the lidar point cloud  210  have been accounted in the classification. A small number of lidar points  214  that are not mapped to any of the objects  312  may be dismissed as artifacts, but a significant number of lidar points  214  that are not mapped to any of the objects  312  may provoke a recomputation, perhaps with adjusted parameters (e.g., different voxel sizes). 
     As a second variation of this fourth aspect, the device may, for respective objects  312 , estimate a position and an orientation of the object  312  according to the lidar points  214  of the lidar point cloud  210  that are associated with the object  312 . Alternatively or additionally, the device may, for respective objects  312 , estimate at least one vector of the object  312  over a time axis of the lidar point cloud (e.g., estimating the speed or acceleration of the object  312  over a period of time). These types of may provide data that may inform, e.g., a prediction of imminent changes in the movement of the object  312 , and/or may facilitate the consolidation of lidar points  214  of the lidar point cloud  210  to respective objects  312 . Additionally, in some variations, the device may subtract the vector of the object  312  from at least one lidar point  214  that is associated with the object  312 , thereby providing information as to the relative movement of the lidar points  214  with respect to the object  312  (e.g., a change of orientation and/or shape of the object  312  during movement). 
     As a third variation of this fourth aspect, the lidar point cloud  210  may be detected by a lidar detector  208  that is mounted on a vehicle  102  having a vehicle vector (e.g., a direction, speed, and/or orientation). The vehicle vector of the vehicle  102  may be subtracted from the vectors of the objects  312  detected in the environment  100 , e.g., in order to discount the movement of the lidar detector  208  from the estimation of movement of the object  312 , thereby translating a mapping of movement relative to the lidar detector  208  to a mapping of movement relative to the environment  100 . 
       FIG. 7  presents an illustration of an exemplary scenario  700  featuring some such variations of this fourth aspect. In this exemplary scenario  700 , after the lidar point cloud  210  is detected, respective lidar points  214  are associated with an object  312  in the environment  100  at various time points  212 . This association may distinguish objects  312  that may otherwise be obscured in the projection  314  of the lidar point cloud  210 ; e.g., two vehicles that are moving in the same direction but closely following each other may result in a conflated projection  314  when aggregated over time, but may be distinguished by associating the lidar points  216  with the identified objects  312  at different time points  212 . Those of ordinary skill in the art may devise many such techniques for further processing the lidar points  214  of the lidar point cloud  210  in accordance with the techniques presented herein. 
     E. Computing Environment 
       FIG. 8  and the following discussion provide a brief, general description of a suitable computing environment to implement embodiments of one or more of the provisions set forth herein. The operating environment of  FIG. 8  is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the operating environment. Example computing devices include, but are not limited to, personal computers, server computers, hand-held or laptop devices, mobile devices (such as mobile phones, Personal Digital Assistants (PDAs), media players, and the like), multiprocessor systems, consumer electronics, mini computers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. 
     Although not required, embodiments are described in the general context of “computer readable instructions” being executed by one or more computing devices. Computer readable instructions may be distributed via computer readable media (discussed below). Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. Typically, the functionality of the computer readable instructions may be combined or distributed as desired in various environments. 
       FIG. 8  illustrates an example of a system  800  comprising a computing device  802  configured to implement one or more embodiments provided herein. In one configuration, computing device  802  includes at least one processing unit  806  and memory  808 . Depending on the exact configuration and type of computing device, memory  808  may be volatile (such as RAM, for example), non-volatile (such as ROM, flash memory, etc., for example) or some combination of the two. This configuration is illustrated in  FIG. 8  by dashed line  804 . 
     In other embodiments, device  802  may include additional features and/or functionality. For example, device  802  may also include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in  FIG. 8  by storage  810 . In one embodiment, computer readable instructions to implement one or more embodiments provided herein may be in storage  810 . Storage  810  may also store other computer readable instructions to implement an operating system, an application program, and the like. Computer readable instructions may be loaded in memory  808  for execution by processing unit  806 , for example. 
     The term “computer readable media” as used herein includes computer storage media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions or other data. Memory  808  and storage  810  are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by device  802 . Any such computer storage media may be part of device  802 . 
     Device  802  may also include communication connection(s)  816  that allows device  802  to communicate with other devices. Communication connection(s)  816  may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting computing device  802  to other computing devices. Communication connection(s)  816  may include a wired connection or a wireless connection. Communication connection(s)  816  may transmit and/or receive communication media. 
     The term “computer readable media” may include communication media. Communication media typically embodies computer readable instructions or other data in a “modulated data signal” such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” may include a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. 
     Device  802  may include input device(s)  814  such as keyboard, mouse, pen, voice input device, touch input device, infrared cameras, video input devices, and/or any other input device. Output device(s)  812  such as one or more displays, speakers, printers, and/or any other output device may also be included in device  802 . Input device(s)  814  and output device(s)  812  may be connected to device  802  via a wired connection, wireless connection, or any combination thereof. In one embodiment, an input device or an output device from another computing device may be used as input device(s)  814  or output device(s)  812  for computing device  802 . 
     Components of computing device  802  may be connected by various interconnects, such as a bus. Such interconnects may include a Peripheral Component Interconnect (PCI), such as PCI Express, a Universal Serial Bus (USB), Firewire (IEEE 1394), an optical bus structure, and the like. In another embodiment, components of computing device  802  may be interconnected by a network. For example, memory  808  may be comprised of multiple physical memory units located in different physical locations interconnected by a network. 
     Those skilled in the art will realize that storage devices utilized to store computer readable instructions may be distributed across a network. For example, a computing device  820  accessible via network  818  may store computer readable instructions to implement one or more embodiments provided herein. Computing device  802  may access computing device  820  and download a part or all of the computer readable instructions for execution. Alternatively, computing device  802  may download pieces of the computer readable instructions, as needed, or some instructions may be executed at computing device  802  and some at computing device  820 . 
     F. Usage of Terms 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. 
     As used in this application, the terms “component,” “module,” “system”, “interface”, and the like are generally intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. 
     Furthermore, the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement the disclosed subject matter. The term “article of manufacture” as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of the claimed subject matter. 
     Various operations of embodiments are provided herein. In one embodiment, one or more of the operations described may constitute computer readable instructions stored on one or more computer readable media, which if executed by a computing device, will cause the computing device to perform the operations described. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Alternative ordering will be appreciated by one skilled in the art having the benefit of this description. Further, it will be understood that not all operations are necessarily present in each embodiment provided herein. 
     Moreover, the word “exemplary” is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as “exemplary” is not necessarily to be construed as advantageous over other aspects or designs. Rather, use of the word exemplary is intended to present concepts in a concrete fashion. As used in this application, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise, or clear from context, “X employs A or B” is intended to mean any of the natural inclusive permutations. That is, if X employs A; X employs B; or X employs both A and B, then “X employs A or B” is satisfied under any of the foregoing instances. In addition, the articles “a” and “an” as used in this application and the appended claims may generally be construed to mean “one or more” unless specified otherwise or clear from context to be directed to a singular form. 
     Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The disclosure includes all such modifications and alterations and is limited only by the scope of the following claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”