Patent Description:
An autonomous driving vehicle refers to a vehicle that recognizes without driver intervention, determines a driving condition, and controls the vehicle to autonomously drive to a given destination. Recently, the autonomous driving vehicle has decreased the number of traffic accidents, enhanced transportation efficiency, saved fuel, and done driving instead and, thus, has attracted attention as individual transportation for enhancing convenience.

For autonomous driving of vehicles, there is a need for various technologies such as a technology for recognizing a driving environment such as a lane, a surrounding vehicle, or a pedestrian, a technology for determining a driving condition, and a control technology such as steering and acceleration/deceleration. Among these, a technology for accurately determining a vehicle position is very important. That is, a detailed map with an error range of a centimeter unit needs to be generated and an accurate position of the vehicle needs to be determined on the generated detailed map.

Technologies of the related art for determining a vehicle position use a global positioning system (GPS), Wi-Fi, or the like but there is a limit due to insufficient accuracy. Accordingly, in order to compensate for the limitation, recently, research has been conducted into a technology using a light detection and ranging (LiDAR) sensor for measuring time during which a laser pulse is emitted and is reflected back to an original position to measure a distance from a reflector.

In this situation, for autonomous driving of various vehicles including cars, there has been a need for a technology for generating a more detailed map of an actual load environment and to more accurately determine a position of a moving object on the detailed map.

<CIT> discloses a method for localizing transportable apparatus within an environment including the steps of: obtaining point cloud data representing a 3D point cloud with appearance information of at least part of the environment, wherein the appearance information comprises colour values for points in the 3D point cloud; obtaining first frame data representing an image produced by a sensor onboard transportable apparatus at a first time and location within the environment and second frame data representing an image produced by the sensor at a second time and location within the environment, harmonizing information about the first frame data, the second frame data and an overlapping subset of the point cloud data in order to determine a location within the point cloud data where at least one of the first frame and the second frame was produced, thereby localizing the transportable apparatus within the environment.

<CIT> discloses methods for correcting an estimated heading using a map, wherein map data indicative of a map of an environment of a vehicle and data indicative of an estimated heading the vehicle is received, a sensor obtains first spatial data indicative of locations of objects in the environment relative to the vehicle at a first time, a first location of the vehicle on the map is determined based on the first spatial data, the sensor obtains second spatial data indicative of locations of objects in the environment relative to the vehicle at a second time, a second location of the vehicle on the map is determined based on the second spatial data, and a heading correction of the vehicle is determined based on the estimated heading, the first location, the first time, the second location, and the second time, and a speed of the vehicle.

Aspects of the present disclosure are to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below. Accordingly, an aspect of the present disclosure is to provide a vehicle and a method of recognizing a position of a vehicle based on a map, for generating a detailed map for autonomous driving of the vehicle and determining an accurate position in the detailed map.

In accordance with an embodiment, a vehicle is provided as defined in the appended claims. In accordance with another embodiment, a method of recognizing a position of a vehicle based on a map is provided as defined in the appended claims.

According to various embodiments of the present disclosure, a detailed map with an error range less than a centimeter unit may be generated and a position of a vehicle may be accurately determined in the generated detailed map. Accordingly, autonomous driving of the vehicle may be easily achieved.

The above and/or other aspects, features, and advantages of certain embodiments of the present disclosure will be more apparent from the following description taken in conjunction with the accompanying drawings, in which:.

Hereinafter, a car will be described as an example of a vehicle, without being limited thereto. For example, a vehicle may include a vessel, a motorcycle, an aircraft, and a train which are capable of being moved with passengers or goods therein. In addition, needless to say, the map generating car or map using car as used herein is also one type of a vehicle.

Certain embodiments and unclaimed aspects of the present disclosure will now be described in greater detail with reference to the accompanying drawings.

<FIG> is a diagram illustrating an example of an autonomous driving system <NUM> according to an embodiment of the present disclosure.

Referring to <FIG>, the autonomous driving system <NUM> includes a map generating car <NUM>, a server <NUM>, and a map using car <NUM>.

The map generating car <NUM> may collect information for generating a map and transmit the information to the server <NUM>. In detail, the map generating car <NUM> may transmit, to the server <NUM>, point cloud information on the ground, which is acquired using a multichannel light detection and ranging (LiDAR) sensor during driving on a target place for generating a map.

In general, a LiDAR sensor refers to a sensor system that emits a high output pulse laser with a specific frequency and measures time taken to receive a reflected wave from an object to acquire information on a distance to the object.

Although there may be various LiDAR technologies, a LiDAR sensor may be a LiDAR sensor that is capable of performing image modeling of a space as well as distance information in a traveling direction of a laser beam. That is, the LiDAR sensor may be a LiDAR sensor that is capable of collecting point cloud information of a ground via point-scanning.

According to the claimed solution, the LiDAR sensor is a multichannel LiDAR sensor that uses a plurality of lasers with different frequencies for respective channels, but not usually a general LiDAR sensor that uses a laser with a single frequency.

Accordingly, as illustrated in <FIG>, in order to generate a map of a road of a specific area, the map generating car <NUM> may scan the road using a laser with a plurality of frequencies while driving on the road of the corresponding area, acquire point cloud information for each frequency channel for the road, and transmit the information to the server <NUM>, via a network <NUM>.

The server <NUM> may generate a multichannel map of the corresponding road using the point cloud information for each channel for the road, received from the map generating car <NUM>. In detail, the server <NUM> may apply various map generating algorithms to the point cloud information for each channel to generate the multichannel map with a plurality of channels and store the generated multichannel map. In this case, the generated multichannel map may be a detailed map containing reflectivity information on each point of a road surface and may be used for autonomous driving.

Although the detailed map generated for autonomous driving is technologically capable of being displayed to a consumer, it would be obvious to one of ordinary skill in the art that the map is used to accurately recognize a vehicle position and is not viewed by the consumer.

The map using car <NUM> may determine a current position based on the multichannel map. In detail, the map using car <NUM> may request the server <NUM> for map data containing the current position and receive corresponding multichannel map data from the server <NUM>. In addition, the map using car <NUM> may acquire point cloud information for each channel of a surrounding ground at the current position of the car <NUM> using the multichannel LiDAR sensor.

For example, the map using car <NUM> may acquire point cloud information for each channel of a ground while rotating by <NUM> degrees the multichannel LiDAR sensor that installed on the map using car <NUM> towards the ground, without being limited thereto.

Accordingly, the map using car <NUM> may match the multichannel map data received from the server <NUM> and the point cloud information on the ground, acquired through the multichannel LiDAR sensor and determine a current position of the map using car <NUM> on the multichannel map.

As such, a map may be generated using the multichannel LiDAR sensor so as to generate a detailed map containing a texture of materials (e.g., a lane or a packaging material) constituting the ground (e.g., a road surface) and an accurate position of a vehicle may be determined on the generated detailed map so as to more accurately determine a position of the vehicle. Accordingly, autonomous driving of a vehicle may become easier.

Hereinafter, generation of a map, i.e., a detailed map by the server <NUM> will be described with reference to <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, and <FIG>.

<FIG> is a diagram illustrating an example of a map generating environment according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, the map generating car <NUM> may collect information for generating a map, i.e., a detailed map. In detail, the map generating car <NUM> may acquire the point cloud information for each channel of a surrounding road (a paved road in the example of <FIG>) of the map generating car <NUM> using the installed multichannel LiDAR sensor and transmit the point cloud information to the server <NUM>. To this end, the map generating car <NUM> may be connected to the server <NUM> through a network <NUM> such as the Internet.

When the map generating car <NUM> transmits the point cloud information on the ground, the server <NUM> may receive the point cloud information, generate a map, and store the generated map. To this end, the server <NUM> may include a communicator <NUM>, a processor <NUM>, and a storage <NUM>.

In detail, the communicator <NUM> may be connected to the network <NUM> and may communicate with an external vehicle via various wired and wireless communication methods. In detail, the communicator <NUM> may communicate with an external vehicle using a distant communication module or a local area communication module. When the distant communication module is used, the communicator <NUM> may communicate with a vehicle according to communication standard such as Institute of Electrical and Electronics Engineers (IEEE), 3rd generation (<NUM>), <NUM> partnership project (3GPP), long term evolution (LTE), and global positioning system (GPS). When the local area communication module is used, the communicator <NUM> may communicate with a vehicle according to communication standard such as Wi-Fi, Bluetooth, near field communication (NFC), ZigBee, and Picocast. In particular, the communicator <NUM> may receive the point cloud information for each channel for the ground, which is acquired and transmitted by the map generating car <NUM>.

The processor <NUM> may control an overall operation of the server <NUM>. In particular, the processor <NUM> may apply various map generating algorithms to the point cloud information for each channel, received through the communicator <NUM>, to generate a multichannel map and store the generated multichannel map in the storage <NUM>. In addition, the processor <NUM> may synthesize the received point cloud information for each channel into single-channel point cloud information and apply a map generating algorithm to the synthesized single-channel point cloud information to generate a single-channel.

As described later, the processor <NUM> may remove influence of ambient light from the point cloud information for each channel, acquired by the map generating car <NUM>, and generate the aforementioned multichannel map or single-channel map.

The generated map data may be stored in the storage <NUM>. As described later, the map data may be transmitted to the map using car <NUM> according to transmission request of the map data of the map using car <NUM>.

A method of selecting a frequency used for each channel of a multichannel LiDAR sensor will be described with reference to <FIG> and <FIG>.

<FIG> and <FIG> are diagrams illustrating an example of a reflectivity of each material type according to a wavelength of an electromagnetic wave according to unclaimed aspects of the present disclosure.

In detail, <FIG> shows reflectivity according to a wavelength of an electromagnetic wave for each type of a surface such as vegetation, dry soil, wet soil, clear lake water, and turbid river water.

<FIG> shows reflectivity according to a wavelength of an electromagnetic wave for each type of a mineral such as calcite, kaolinite, hematite, and montmorillonite.

Referring to <FIG> and <FIG>, reflectivity of an electromagnetic wave may be varied according to a type of a surface or mineral.

When materials have the same reflectivity in a specific wavelength, the materials are not capable of being distinguished through a laser with a frequency of the wavelength. That is, as a reflectivity difference between materials in a predetermined wavelength is larger, the materials may be easily distinguished via laser with the frequency of the wavelength.

Thus, when a laser frequency of each channel of a multichannel LiDAR sensor is selected, a frequency with a large reflectivity difference between the materials constituting the ground may be selected.

For example, in order to configure a <NUM>-channel LiDAR sensor, a wavelength of <FIG> may be classified into three regions (<NUM> to <NUM> micrometer periods, <NUM> to <NUM> micrometer periods, and <NUM> to <NUM> micrometer periods), variance of reflectivity values of surface types for respective wavelengths of an electromagnetic wave may be calculated for each of the three regions, and then wavelengths with a highest variance value may be selected. Reciprocals of the three selected wavelengths may be frequencies of a laser of each channel of the <NUM>-channel LiDAR sensor.

In addition, for example, in order to configure a <NUM>-channel LiDAR sensor, a wavelength of <FIG> may be classified into four regions, standard deviation of reflectivity values of minerals for respective wavelengths may be calculated for each of the four regions, and then wavelengths with a highest standard deviation value may be selected so as to configure each channel layer frequency of the <NUM>-channel LiDAR sensor.

However, an example of selecting a multichannel frequency is not limited thereto. For example, the number of channels may be two or five or more. In addition, reflectivity data according to a frequency (or a wavelength) of materials is not necessarily divided into the number of channels, and thus, for example, a reflectivity difference (e.g., variance) between materials may be calculated for all frequencies, and then frequencies may be selected by as much as the number of channels in an order from a highest difference.

A laser of the selected frequency may be used in a multichannel LiDAR sensor so as to clearly distinguish materials constituting the ground, and accordingly, it may be possible to generate a more detailed map and to accurately determine a position of a vehicle on the map.

<FIG>, <FIG> are diagrams illustrating an example of a procedure of generating a map by servers <NUM>-<NUM> and <NUM>-<NUM> according to unclaimed aspects of the present disclosure.

<FIG> is a diagram illustrating an example of generation of a multichannel map.

Referring to <FIG>, a map generating car <NUM>-<NUM> may include a LiDAR sensor <NUM>-<NUM> and a communicator <NUM>-<NUM>. Basic components of a car, constituting the map generating car <NUM>-<NUM>, for example, a steering device, an acceleration device, a deceleration device, a door, and an electronic control unit (ECU) are not related to the essence of the disclosure and, thus, will not be illustrated or described.

The LiDAR sensor <NUM>-<NUM> may acquire point cloud information on a surrounding ground of the map generating car <NUM>-<NUM> using a multichannel laser. In this case, the point cloud information may include poser information and reflectivity information of each sampling point of the ground.

To this end, the LiDAR sensor <NUM>-<NUM> may include a laser transmitter (not shown) and a laser receiver (not shown) for each channel. In addition, the LiDAR sensor <NUM>-<NUM> may be installed at an appropriate position for scanning of the surrounding ground of the vehicle, for example, at an upper end portion of the map generating car <NUM>-<NUM> so as to emit a laser beam toward the ground, without being limited thereto.

The communicator <NUM>-<NUM> may communicate with an external device. In detail, the communicator <NUM>-<NUM> may communicate with the external device (e.g., a cloud server) <NUM>-<NUM> positioned outside the map generating car <NUM>-<NUM> using a distant communication module or a local area communication module. When the distant communication module is used, the communicator <NUM>-<NUM> may communicate with the server <NUM>-<NUM> according to communication standard such as IEEE, <NUM>, 3GPP, LTE, and GPS. When the local area communication module is used, the communicator <NUM>-<NUM> may communicate with the server <NUM>-<NUM> according to communication standard such as Wi-Fi, Bluetooth, NFC, ZigBee, and Picocast. In particular, the communicator <NUM>-<NUM> may transmit the point cloud information for each channel, acquired through the LiDAR sensor <NUM>-<NUM>, to the server <NUM>-<NUM>.

In response to the point cloud information for each channel being received from the map generating car <NUM>-<NUM> through a communicator <NUM>-<NUM>, the server <NUM>-<NUM> may generate a multichannel map using the received information and the multichannel map in a storage <NUM>-<NUM>.

In detail, a processor <NUM>-<NUM> may apply a simultaneous localization and mapping (SLAM) algorithm to the point cloud information for each channel, received through the communicator <NUM>-<NUM>. The point cloud information for each channel, which is acquired with respect to the ground by the map generating car <NUM>-<NUM> through the SLAM <NUM>-<NUM> algorithm, may be optimized so as to estimate pose information. In this case, the pose information may include x and y coordinates and direction information on a map to be generated.

Sensor information is present in each estimated pose information item and, thus, the processor <NUM>-<NUM> may back-project sensor information for each pose and perform grid mapping <NUM>-<NUM>. As the result of the grid mapping <NUM>-<NUM>, a multichannel map <NUM>-<NUM> may be generated.

In this case, the generated multichannel map <NUM>-<NUM> may be a multichannel 2D reflectivity map containing channels corresponding to respective channels of the multichannel LiDAR sensor <NUM>-<NUM> installed in the map generating car <NUM>-<NUM>. In detail, the multichannel map <NUM>-<NUM> may include pixel-unit reflectivity information for each channel and may be a detailed map containing a lane position or texture information of a road on which the map generating car <NUM>-<NUM> drives. The processor <NUM>-<NUM> may store the aforementioned generated multichannel map <NUM>-<NUM> in the storage <NUM>-<NUM>.

The reflectivity of a road surface may be changed according to time and, thus, the processor <NUM>-<NUM> may store a mean value or variance of reflectivity in the multichannel map.

In detail, the map generating car <NUM>-<NUM> may drive on the same road a plurality of numbers of times and transmit point cloud information for each of a plurality of channels with respect to the same point to the server <NUM>-<NUM>, and the server <NUM>-<NUM> may store a mean or variance value together for each channel in the multichannel map.

<FIG> illustrates a procedure of generating a single-channel map by the server <NUM>-<NUM> according to an unclaimed aspect of the present disclosure. A map generating car <NUM>-<NUM> of <FIG> is generally the same as the map generating car <NUM>-<NUM> of <FIG>.

In response to point cloud information for each channel being received from the map generating car <NUM>-<NUM> through a communicator <NUM>-<NUM>, the server <NUM>-<NUM> may generate a single-channel map using the received information and store the single-channel map in a storage <NUM>-<NUM>.

In detail, in response to the point cloud information for each channel being received through the communicator <NUM>-<NUM>, a processor <NUM>-<NUM> may synthesize the point cloud information for each channel into one channel. In <FIG>, decolorization <NUM>-<NUM> may refer to such processing by the processor <NUM>-<NUM>, that is, an operation of synthesizing multichannel point cloud information into single-channel point cloud information.

The processor <NUM>-<NUM> may multiply each channel constituting a multichannel by a fixed weight to synthesize one channel. For example, in the case of point cloud information received through a <NUM>-channel LiDAR sensor, point cloud information items for respective channels may be multiplied by a weight of <NUM>% and summed to synthesize single-channel point cloud information.

However, just because information is acquired through N channels does not mean each channel needs to be multiplied by a weight of <NUM>/N. For example, in the case of point cloud information received through a <NUM>-channel LiDAR sensor, the processor <NUM>-<NUM> may multiply information acquired through respective channels by fixed weights, and for example, multiply information acquired through a first channel by <NUM>%, multiply information acquired through a second channel by <NUM>%, and multiply information acquired through a third channel by <NUM>%, and sum the results to synthesize single-channel point cloud information.

In this case, as described above, when a frequency of each channel constituting a multichannel is selected, a channel with a large reflectivity difference between materials may be multiplied by a high weight. That is, in the above example, a reflectivity difference between materials in a frequency of a third channel may be largest and a reflectivity difference between materials in a frequency of a first channel may be smallest. That is, for example, the processor <NUM>-<NUM> may calculate variance or standard deviation for each channel of the point cloud information for each channel and multiply a channel with a greater calculated variance value or standard deviation by a greater weight.

The processor <NUM>-<NUM> may adaptively change a weight for each scan of a multichannel LiDAR sensor <NUM>-<NUM> and multiply each channel by a weight to synthesize single-channel point cloud information. Here, the scan may be information acquired during one operation of the LiDAR sensor <NUM>-<NUM>, and for example, may be point cloud information acquired during one rotation, i.e., <NUM>°of the LiDAR sensor <NUM>-<NUM>, or <NUM>/<NUM> rotation, i.e., <NUM>°, without being limited thereto.

For example, the processor <NUM>-<NUM> may calculate variance values of channel information items for respective scans, normalize the channel information items by as much as the variance values, and then sum the normalized channel information items to synthesize single-channel point cloud information. However, the processor <NUM>-<NUM> may calculate variance or standard deviation values of reflectivity information contained in point cloud information for each channel for respective scans and multiply a channel with a greater calculated variance or standard deviation value by a higher weight to synthesize single-channel point cloud information.

<FIG> is a diagram illustrating a concept of an example in which the processor <NUM>-<NUM> performs the decolorization <NUM>-<NUM> as described above when <NUM>-channel point cloud information is received through the communicator <NUM>-<NUM>. Weights <NUM> to <NUM> to be multiplied to each channel information item may be fixed or may be adaptively changed for each scan, as described above.

Thus far, when a channel is multiplied by a weight, this means that reflectivity information for each channel contained in the point cloud information for each channel is multiplied by a weight, without being limited thereto.

Thus, when multichannel point cloud information is synthesized into single-channel point cloud information, as illustrated in <FIG>, the processor <NUM>-<NUM> may apply an SLAM <NUM>-<NUM> algorithm to single-channel point cloud information and perform grid mapping <NUM>-<NUM> to generate a map, and in this case, the generated map may be a single-channel map <NUM>-<NUM>. The processor <NUM>-<NUM> may store the generated single-channel map <NUM>-<NUM> in the storage <NUM>-<NUM>.

Referring to <FIG>, the example in which the processor <NUM>-<NUM> of the server <NUM>-<NUM> performs the decolorization <NUM>-<NUM> has been described thus far, the present disclosure is not limited thereto.

For example, a processor (not shown) included in the map generating car <NUM>-<NUM> may perform decolorization on the point cloud information for each channel, acquired through the multichannel LiDAR sensor <NUM>-<NUM>, as described above. Accordingly, when the point cloud information for each channel is synthesized into single-channel point cloud information, the map generating car <NUM>-<NUM> may transmit the synthesized single-channel point cloud information to the server <NUM>-<NUM> through a communicator <NUM>-<NUM>.

Accordingly, the server <NUM>-<NUM> may receive single-channel point cloud information from the map generating car <NUM>-<NUM> through the communicator <NUM>-<NUM>, apply the SLAM <NUM>-<NUM> algorithm to the received single-channel point cloud information, and perform the grid mapping <NUM>-<NUM> to generate the single-channel map <NUM>-<NUM>. In this case, the processor <NUM>-<NUM> of the server <NUM>-<NUM> may not necessarily perform the decolorization <NUM>-<NUM> separately.

<FIG> and <FIG> are diagrams illustrating an example of a procedure in which servers <NUM>-<NUM> and <NUM>-<NUM> remove reflected light from multichannel point cloud information and generates a map according to unclaimed aspects of the present disclosure. Here, the reflected light may be obtained by reflecting light of a light source, for example, the sun or a streetlamp and may distort LiDAR sensor information, thereby degrading accuracy of map generation or position recognition. That is, since a LiDAR sensor <NUM> of the map generating car <NUM> may scan a surrounding ground of the car <NUM> to acquire multichannel point cloud information, in this case, reflected light reflected by the surrounding ground may distort information acquired through the LiDAR sensor <NUM>. Accordingly, it may be necessary to remove influence of the reflected light from information acquired through the LiDAR sensor <NUM>.

Referring to <FIG>, in response to multichannel point cloud information being received through a communicator <NUM>-<NUM>, a processor <NUM>-<NUM> of the server <NUM>-<NUM> may perform rasterization <NUM>-<NUM> on the received multichannel point cloud information to acquire 2D data. Since the point cloud information acquired through the LiDAR sensor is three-dimensional (3D) information, it may be necessary to convert 3D information into 2D information in order to remove reflected light. Accordingly, the processor <NUM>-<NUM> may rasterize 3D information for each channel to 2D information for each channel.

Accordingly, the processor <NUM>-<NUM> may perform reflected-light removal <NUM>-<NUM> from the rasterized 2D data for each channel. In detail, the processor <NUM>-<NUM> may apply various algorithms for removing reflected light to the 2D data. For example, the processor <NUM>-<NUM> may apply at least one of analyzing methods including color space analysis for specularity removal, image-plane spatial analysis, and video sequence analysis to the rasterized 2D data to perform reflected-light removal.

As such, when reflected light is removed from the 2D data for each channel, the processor <NUM>-<NUM> may apply an SLAM <NUM>-<NUM> algorithm to the 2D data for each channel, from which the reflected light is removed and perform grid mapping <NUM>-<NUM> to generate a multichannel map <NUM>-<NUM> from which influence of the reflected light is removed, as described with reference to <FIG>.

Decolorization <NUM>-<NUM> may be performed on the 2D data for each channel, from which reflected light is removed, to synthesize a single-channel, an SLAM <NUM>-<NUM> algorithm may be applied to the single-channel, and grid mapping <NUM>-<NUM> may be performed to generate a single-channel map <NUM>-<NUM> from which influence of reflected light is removed. In this case, when a processor (not shown) included in a map generating car may perform decolorization on the point cloud information for each channel and transmit the point cloud information for each channel to the server <NUM>-<NUM>, the processor <NUM>-<NUM> of the server <NUM>-<NUM> may perform the rasterization <NUM>-<NUM> on the single-channel point cloud information received through the communicator <NUM>-<NUM> to perform the reflected-light removal <NUM>-<NUM> and then generate the single-channel map <NUM>-<NUM> through the SLAM <NUM>-<NUM> and the grid mapping <NUM>-<NUM> without needing to re-perform the decolorization <NUM>-<NUM>.

As such, the generated multichannel map <NUM>-<NUM> or the single-channel map <NUM>-<NUM> may be stored in a storage <NUM>-<NUM>.

<FIG> illustrates an example of generation of a map from which influence of reflected light is removed according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, a map generating car <NUM>-<NUM> may include a LiDAR sensor <NUM>-<NUM>, a communicator <NUM>-<NUM>, the processor <NUM>-<NUM>, an ambient light sensor <NUM>-<NUM>, a GPS receiver <NUM>-<NUM>, and an inertial sensor (not shown).

The LiDAR sensor <NUM>-<NUM> may transmit a plurality of laser beams with a plurality of frequencies toward a surrounding ground of the map generating car <NUM>-<NUM> during driving of a vehicle (the map generating car <NUM>-<NUM>) to acquire multichannel point cloud information on the surrounding ground.

The ambient light sensor <NUM>-<NUM> may detect ambient light of the vehicle. For example, the ambient light sensor <NUM>-<NUM> may detect intensity of sunlight that shines the map generating car <NUM>-<NUM>. To this end, the ambient light sensor <NUM>-<NUM> may include various illuminance sensors without being limited thereto. In addition, a position of the vehicle, in which the ambient light sensor <NUM>-<NUM> is installed, may not also be limited to a specific position and, thus, the ambient light sensor <NUM>-<NUM> may be installed at a position so as to experimentally and easily detect intensity of ambient light.

The GPS receiver <NUM>-<NUM> may receive a GPS signal and calculate positional information of the vehicle. In detail, the GPS receiver <NUM>-<NUM> may receive the GPS signal transmitted from a preset number (e.g., three or more) of satellites and calculate current positional information of the vehicle. The processor <NUM>-<NUM> may calculate a current position of the vehicle based on the received GPS signal.

The inertial sensor (not shown) may determine a progress direction of a vehicle during movement of the vehicle.

The processor <NUM>-<NUM> may determine a position of the sun based on a position of the vehicle. In detail, the processor <NUM>-<NUM> may recognize a current position of the sun on the earth based on information on the current weather and time. In addition, the processor <NUM>-<NUM> may recognize the current position of the vehicle on the earth through the GPS receiver <NUM>-<NUM> and recognize the progress direction of the vehicle through the inertial sensor (not shown) and, thus, may determine the position of the sun based on the position of the vehicle.

For example, the processor <NUM>-<NUM> may determine a current position of the sun, that is, which side the sun is positioned among left, right, front, rear, and central directions of the map generating car <NUM>-<NUM> and generate information on the determined position of the sun.

The processor <NUM>-<NUM> may control the communicator <NUM>-<NUM> to transmit, to the server <NUM>-<NUM>, the point cloud information for each channel, acquired through the multichannel LiDAR sensor <NUM>-<NUM>, the ambient light information (e.g., information on intensity of sunlight) detected through the ambient light sensor <NUM>-<NUM>, and the aforementioned generated information on the position of the sun based on the current position of the vehicle.

In response to information being received from the map generating car <NUM>-<NUM> through a communicator <NUM>-<NUM>, the server <NUM>-<NUM> may generate a multichannel map or single-channel map from which reflected light is removed using the received information and store the same in the storage <NUM>-<NUM>.

In detail, as described above, a processor <NUM>-<NUM> may perform rasterization <NUM>-<NUM> on the received multichannel point cloud information in order to remove reflected light from the point cloud information for each channel. Then, the processor <NUM>-<NUM> may apply various algorithms for removing reflected light to the rasterized 2D data for each channel to perform reflected-light removal <NUM>-<NUM>.

In this case, the processor <NUM>-<NUM> may perform the reflected-light removal <NUM>-<NUM> using at least one of ambient light information and information on a position of the sun, received from the map generating car <NUM>-<NUM>.

For example, when intensity of ambient light (e.g., sunlight) is high, the processor <NUM>-<NUM> may increase intensity of the reflected-light removal <NUM>-<NUM>, and when the intensity of the ambient light is low, the processor <NUM>-<NUM> may reduce the intensity of the reflected-light removal <NUM>-<NUM>. However, an example in which the reflected-light removal <NUM>-<NUM> is performed using ambient light is not limited thereto, and for example, when intensity of sunlight is preset intensity or more, the reflected-light removal <NUM>-<NUM> may be performed, and when the intensity of sunlight is less than present intensity, the reflected-light removal <NUM>-<NUM> may not be performed.

For example, when the sun is positioned at front left of the map generating car <NUM>-<NUM>, the possibility that reflected light is present at front left of the map generating car <NUM>-<NUM> is high, and thus, the processor <NUM>-<NUM> may increase the intensity of the reflected-light removal <NUM>-<NUM> in a region corresponding to a front left side of the map generating car <NUM>-<NUM> in the rasterized 2D data for each channel. In this manner, the processor <NUM>-<NUM> may apply information on a position of the sun and perform the reflected-light removal <NUM>-<NUM>.

As such, when reflected light is removed from the 2D data for each channel, the processor <NUM>-<NUM> may perform the SLAMs <NUM>-<NUM> and <NUM>-<NUM> and the grid mapping <NUM>-<NUM> and <NUM>-<NUM> to generate the multichannel map <NUM>-<NUM> or the single-channel map <NUM>-<NUM>, as described with reference to <FIG>. In this case, as described above, in order to generate the single-channel map <NUM>-<NUM>, the decolorization <NUM>-<NUM> may be performed by the processor <NUM>-<NUM> of the server <NUM>-<NUM> or the processor <NUM>-<NUM> of the map generating car <NUM>-<NUM>.

<FIG> are flowcharts of a method of generating a map according to unclaimed aspects of the present disclosure.

<FIG> is a flowchart of a method of generating a multichannel map according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, the map generating car <NUM> (illustrated in <FIG>) may acquire the point cloud information for each channel on the ground through a multichannel LiDAR sensor while driving on a place as a target for generating a multichannel map in operation S610-<NUM>.

Accordingly, when the map generating car <NUM> transmits the point cloud information for each channel to the server <NUM>, the server <NUM> may generate a multichannel map using the received point cloud information for each channel in operation S610-<NUM>. In detail, the server <NUM> may apply various simultaneous localization and mapping (SLAM) algorithms to the point cloud information for each channel and perform grid mapping to generate a multichannel map of the ground of a corresponding place. As such, the generated multichannel map may be stored in the storage <NUM> of the server <NUM> and, then, may be transmitted to the map using car <NUM> according to a request of the map using car <NUM>.

<FIG> is a flowchart of a method of generating a single-channel map according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, the map generating car <NUM> (illustrated in <FIG>) may acquire the point cloud information for each channel on the ground through a multichannel LiDAR sensor while driving on a place as a target for generating a single-channel map in operation S610-<NUM>. Accordingly, the map generating car <NUM> may transmit the acquired point cloud information for each channel to the server <NUM> (illustrated in <FIG>).

Accordingly, the server <NUM> may perform decolorization of multiplying the received point cloud information for each channel by a weight for each channel to synthesize point cloud information of one channel in operation S620-<NUM>. In addition, the server <NUM> may apply a SLAM algorithm to the single-channel point cloud information synthesized through decolorization and perform grid mapping to generate a single-channel map of the ground of a corresponding place in operation S630-<NUM>.

Decolorization may be performed by the map generating car <NUM>. In detail, the map generating car <NUM> may multiply the multichannel point cloud information acquired through the multichannel LiDAR sensor by a weight for each channel to synthesize single-channel point cloud information and transmit the synthesized single-channel point cloud information to the server <NUM>.

Accordingly, the server <NUM> may apply a SLAM algorithm directly to the single-channel point cloud information received from the map generating car <NUM> and perform grid mapping to generate a single-channel map without needing to perform separate decolorization in operation S620-<NUM>.

<FIG> and <FIG> are flowcharts of a method of removing or reducing distortions, such as reflected light from information acquired through a LiDAR sensor and generating a map according to unclaimed aspects of the present disclosure.

Referring to <FIG>, the map generating car <NUM> (illustrated in <FIG>) may acquire point cloud information for each channel on the ground through the multichannel LiDAR sensor while driving to a place as a target for generating a map in operation S710-<NUM>. As such, the acquired multichannel point cloud information may be transmitted to the server <NUM> (illustrated in <FIG>) without changes or may be synthesized into single-channel point cloud information via the aforementioned decolorization and transmitted to the server <NUM>.

Since the information acquired through the LiDAR sensor is 3D, it may be necessary to convert the received 3D information into 2D information in order to remove reflected light. Accordingly, the server <NUM> may perform rasterization on the received multichannel point cloud information or single-channel point cloud information to generate 2D data in operation S720-<NUM>.

Accordingly, the server <NUM> may apply various algorithms for removing or minimizing the reflected light to the rasterized multichannel or single-channel 2D data to perform reflected-light removal or reduction in operation S730-<NUM>.

Then, the server <NUM> may apply an SLAM algorithm to 2D data on which reflected-light removal or reduction is performed and perform grid mapping to generate a multichannel map or a single-channel map of the ground of a corresponding place (S740-<NUM>).

<FIG> is a flowchart of a method for generating a map from which reflected light is removed according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, the map generating car <NUM> (illustrated in <FIG>) may acquire point cloud information for each channel on the ground through a multichannel LiDAR sensor while driving on a place as a target for generating a map in operation S710-<NUM>. In addition, the map generating car <NUM> may detect intensity of ambient light through an ambient light sensor in operation S720-<NUM>. In this case, the ambient light may be sunlight without being limited thereto.

The map generating car <NUM> may calculate positional information of the sun in operation S730-<NUM>. In detail, the map generating car <NUM> may determine a position of the sun based on a position of the map generating car <NUM> using information on current weather and time, information on a current position of the map generating car <NUM> through a GPS receiver, and information on a moving direction of the map generating car <NUM> through an inertial sensor.

Accordingly, the map generating car <NUM> may transmit, to the server <NUM> (illustrated in <FIG>), the multichannel point cloud information on the ground, ambient light information, and information on a position of the sun based on the position of the map generating car <NUM>.

The server <NUM> may perform rasterization to the multichannel point cloud information received from the map generating car <NUM> to generate 2D data in operation S740-<NUM> and apply various algorithms for removing reflected light to the rasterized 2D data in operation S750-<NUM>. In this case, the server <NUM> may adjust intensity of ambient-light removal or reduction by using ambient light information, for example, information on intensity of sunlight. In addition, the server <NUM> may apply information on a position of the sun when an algorithm for removing or reducing ambient light is applied and perform reflected-light removal.

Accordingly, the server <NUM> may apply an SLAM algorithm to 2D data from which reflected light is removed and perform grid mapping to generate a map of the ground of a corresponding place in operation <NUM>-<NUM>. In this case, the map may be generated as a multichannel map or a single-channel map according to decolorization is further performed in each operation, as described above.

Although the example in which the server <NUM> generates a map has been described with reference to <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, and <FIG>, the present disclosure is not limited thereto and the map generating car <NUM> may directly generate a map using the point cloud information for each channel, acquired through a LiDAR sensor, transmit the generated map to the server <NUM>, and store the same in the server <NUM>.

As described above, the map generated through the server <NUM> or the map generating car <NUM> may be a detailed map containing reflectivity information on each point of the ground (e.g., a road surface), may have a slight error of a unit of centimeter compared with an actual ground, and may be used to accurately recognize a position of the map using car <NUM>, which will be described later. Accordingly, autonomous driving may be more easily performed.

Hereinafter, embodiments and unclaimed aspects in which the map using car <NUM> accurately recognizes a position using a generated map as described above will be described with reference to <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG> and <FIG>.

<FIG> is a diagram illustrating an example of a position recognition environment of a vehicle according to an embodiment of the present disclosure.

Referring to <FIG>, the map using car <NUM> recognizes an accurate position of the map using car <NUM> in a map based on the map.

In detail, the map using car <NUM> is connected to the server <NUM> through various networks <NUM> such as the Internet and receives map data containing a place on which the map using car <NUM> currently drives from the server <NUM>. In this case, the map data received from the server <NUM> may refer to map data of the map generated by the server <NUM> or the map generating car <NUM>, as described above with reference to <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, and <FIG>.

In addition, the map using car <NUM> acquires point cloud information for each channel on a surrounding ground (a paved road in the example of <FIG>) of the car <NUM> using a multichannel LiDAR sensor installed in the map using car <NUM>.

Accordingly, the map using car <NUM> maps the map data received from the server <NUM> and acquired through the LiDAR sensor to determine a current position of the map using car <NUM> in the map.

Here, the map using car <NUM> may be any vehicle that recognizes a position based on the map received through the external server <NUM> and uses the recognized position as information for autonomous driving.

<FIG> is a block diagram illustrating a configuration of the map using car <NUM> according to an embodiment of the present disclosure.

Referring to <FIG>, the map using car <NUM> includes a LiDAR sensor <NUM>, a processor <NUM>, and a communicator <NUM>. General basic car components constituting the map using car <NUM>, for example, a steering device, an acceleration device, a deceleration device, a door, and an ECU are not necessarily related to the essence of the embodiments of the disclosure and, thus, such components will not be further illustrated or described.

The LiDAR sensor <NUM> is the same as the LiDAR sensors <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-<NUM> which have been described with reference to <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, and <FIG> and, thus, a repeated description thereof will not be provided.

However, a frequency for each channel of the LiDAR sensor <NUM> used in the map using car <NUM> may be the same as a frequency for each channel of a LiDAR sensor installed in the map generating car <NUM> used to generate map data received through the communicator <NUM>, as described below.

That is, for example, the map generating car <NUM> may include a <NUM>-channel LiDAR sensor including a first channel using a laser of a first frequency, a second channel using a laser of a second frequency, and a third channel using a laser of a third frequency and acquire <NUM>-channel point cloud information using the <NUM>-channel LiDAR sensor. As such, when the <NUM>-channel point cloud information is used to generate a <NUM>-channel map or a single-channel map, if the map using car <NUM> receives the <NUM>-channel map or single-channel map data from the server <NUM> through the communicator <NUM>, the LiDAR sensor <NUM> that needs to be included in the map using car <NUM> in order to recognize a position based on the received map may be a <NUM>-channel LiDAR sensor including a first channel using a laser of a first frequency, a second channel using a laser of a second frequency, and a third frequency using a laser of a third frequency, such as a LiDAR sensor of the map generating car <NUM>.

As such, the LiDAR sensor <NUM> acquires point cloud information for each channel on a surrounding ground of the map using car <NUM> using a multichannel laser.

The communicator <NUM> communicates with an external server to transmit and receive various information items. In detail, the communicator <NUM> may request the external server <NUM> to transmit map data and receive the map transmitted in response to the request of the external server <NUM> under control of the processor <NUM>.

To this end, the communicator <NUM> may be connected to the network <NUM> via various wired and wireless communication methods and may communicate with the server <NUM>. In detail, the communicator <NUM> may communicate with the external server <NUM> using a distant communication module or a local area communication module. When the distant communication module is used, the communicator <NUM> may communicate with the external server <NUM> according to communication standard such as IEEE, <NUM>, 3GPP, LTE, and GPS. When the local area communication module is used, the communicator <NUM> may communicate with a vehicle according to communication standard such as Wi-Fi, Bluetooth, NFC, ZigBee, and Picocast.

The processor <NUM> controls an overall operation of the map using car <NUM>. In particular, the processor <NUM> controls the communicator <NUM> to receive map data from the external server <NUM>. In addition, the processor <NUM> controls the LiDAR sensor <NUM> to acquire multichannel point cloud information on a ground (e.g., a road surface) on which the map using car <NUM> drives. Accordingly, the processor <NUM> determines a position of the map using car <NUM> in the map received from the external server <NUM> based on the point cloud information for each channel, acquired through the LiDAR sensor <NUM>.

Hereinafter, various embodiments and unclaimed aspects in which the map using car <NUM> determines a position of the map using car <NUM> in the map data received from the server <NUM> will be described in detail with reference to <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, <FIG>, and <FIG>.

<FIG> illustrates an example in which the map using car <NUM> determines a position based on a multichannel map according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, a processor <NUM>-<NUM> of the map using car <NUM> (illustrated in <FIG>) may match multichannel map data <NUM>-<NUM> received from the server <NUM> (illustrated in <FIG>) through the communicator <NUM> (illustrated in <FIG>) and the multichannel point cloud information <NUM>-<NUM> acquired through the LiDAR sensor <NUM> to determine in operation <NUM>-<NUM> a localized position of the map using car <NUM> in the multichannel map.

In this case, the multichannel map may be reflectivity information corresponding to the number of channels for each point on a map. That is, for example, in the case of a <NUM>-channel map, a specific point on the map may include three reflectivity information items. The LiDAR sensor <NUM> also acquires point cloud information for each channel for every scan point and, thus, for example, in the case of a three-channel LiDAR sensor, three-point cloud information items may be acquired for every scan point.

Accordingly, the processor <NUM>-<NUM> may map the multichannel point cloud information acquired through the LiDAR sensor <NUM> and the multichannel map to determine a current position of the map using car <NUM> in the multichannel map. In particular, a plurality of channel information items is jointed and compared with each other and thus, a position in a map may be more accurately specified.

Accordingly, the processor <NUM>-<NUM> may generate current positional information <NUM>-<NUM> of the map using car <NUM> in multichannel map data. In this case, the positional information <NUM>-<NUM> may include x and y coordinates and direction information of the map using car <NUM> in the multichannel map without being limited thereto.

<FIG> illustrates an example in which the map using car <NUM> (illustrated in <FIG>) determines a position based on a single-channel map according to an embodiment of the present disclosure.

Referring to <FIG>, a processor <NUM>-<NUM> of the map using car <NUM> determines in operation <NUM>-<NUM> a localized position of the map using car <NUM> (illustrated in <FIG>) in the single-channel map data <NUM>-<NUM> using the single-channel map data <NUM>-<NUM> received from the server <NUM> through the communicator <NUM> and multichannel point cloud information <NUM>-<NUM> acquired through the LiDAR sensor <NUM>.

In detail, the processor <NUM>-<NUM> multiplies multichannel point cloud information acquired through the LiDAR sensor <NUM> with a weight for each channel to synthesize single-channel point cloud information. This procedure is referred to as decolorization <NUM>-<NUM>. The decolorization <NUM>-<NUM> is the same as in the description of generation of a single-channel map with reference to <FIG> and, thus, a repeated description thereof will not be provided.

In this case, when the single-channel map <NUM>-<NUM> is generated by multiplying each channel with a fixed weight, a weight that is multiplied to each channel of the multichannel point cloud information acquired through the LiDAR sensor <NUM> by the processor <NUM>-<NUM> of the map using car <NUM> may be the same as a weight applied in a generating procedure of the single-channel map <NUM>-<NUM>, without being limited thereto. To this end, the server <NUM> may also transmit information on a weight applied to a map generating procedure during transmission of the single-channel map <NUM>-<NUM>.

Accordingly, the processor <NUM>-<NUM> matches a single-channel map <NUM>-<NUM> and single-channel point cloud information to determine in operation <NUM>-<NUM> a position of the map using car <NUM> and generate positional information <NUM>-<NUM>.

<FIG> illustrates an operation of the processor <NUM> that determines a position of the map using car <NUM> when the map using car <NUM> (illustrated in <FIG>) receives a generated map from which reflected light is removed like in the example of <FIG> according to an unclaimed aspect of the present disclosure. Further, <FIG> illustrates an operation of the processor <NUM> that determines a position of the map using car <NUM> when the map using car <NUM> (illustrated in <FIG>) receives a generated map from which reflected light is removed like in the example of <FIG> according to an embodiment of the present disclosure.

In detail, <FIG> illustrates a case in which the map using car <NUM> receives a multichannel map <NUM>-<NUM> from which reflected light is removed. In this case, a processor <NUM>-<NUM> may convert multichannel point cloud information <NUM>-<NUM> acquired through the LiDAR sensor <NUM> into 2D information using rasterization <NUM>-<NUM>. Then, the processor <NUM>-<NUM> may apply in operation <NUM>-<NUM> the various algorithms for removing reflected-light described with reference to <FIG> to the rasterized 2D information to generate multichannel 2D information from which reflected light is removed and matched to the multichannel 2D information and the multichannel map <NUM>-<NUM> from which reflected light is removed to determine in operation <NUM>-<NUM> a localized position of the map using car <NUM> in the multichannel map <NUM>-<NUM>. Thus, the processor <NUM>-<NUM> may generate positional information <NUM>-<NUM> of the map using car <NUM>.

<FIG> illustrates a case in which the map using car <NUM> (illustrated in <FIG>) receives a single-channel map <NUM>-<NUM> from which reflected light is removed. In this case, a processor <NUM>-<NUM> performs decolorization <NUM>-<NUM> on multichannel point cloud information <NUM>-<NUM> acquired through the LiDAR sensor <NUM> to synthesize single-channel point cloud information. Then, the processor <NUM>-<NUM> converts the synthesized single-channel point cloud information into 2D information using rasterization <NUM>-<NUM> and apply in operation <NUM>-<NUM> the various algorithms for removing reflected light described with reference to <FIG> to the rasterized 2D information to generate 2D information from which reflected light is removed.

Accordingly, the processor <NUM>-<NUM> matches the single-channel map <NUM>-<NUM> from which reflected light is removed and the rasterized 2D information from which reflected light is removed to determine in operation <NUM>-<NUM> a localized position of the map using car <NUM> in the single-channel map <NUM>-<NUM>. Accordingly, the processor <NUM>-<NUM> generates positional information <NUM>-<NUM> of the map using car <NUM>.

As such, the generated positional information <NUM>-<NUM>, <NUM>-<NUM>, <NUM>-<NUM>, and <NUM>-<NUM> may be used as information for autonomous driving.

<FIG> is a block diagram illustrating components of a vehicle according to an embodiment of the present disclosure.

Referring to <FIG>, a map using car <NUM>-<NUM> includes a LiDAR sensor <NUM>-<NUM>, a processor <NUM>-<NUM>, a communicator <NUM>-<NUM>, and optionally a GPS receiver <NUM>-<NUM>. As described with reference to <FIG>, the same components of <FIG> as those of the map using car <NUM> illustrated in <FIG> will not be repeatedly described and thus, a repeated description will not be provided.

The GPS receiver <NUM>-<NUM> may receive a GPS signal and determine a position of the map using car <NUM>-<NUM>. In detail, the GPS receiver <NUM>-<NUM> may receive a GPS signal from a preset number of external GPS satellites and calculate a position of the map using car <NUM>-<NUM> based on the received GPS signal. The processor <NUM>-<NUM> may calculate the position of the map using car <NUM>-<NUM> based on the received GPS signal, needless to say.

The processor <NUM>-<NUM> may primarily determine the position of the map using car <NUM> in the map data received from the server <NUM> based on the GPS signal received through the GPS receiver <NUM>-<NUM>. Then, the processor <NUM>-<NUM> may secondarily determine the position of the map using car <NUM>-<NUM> using the multichannel point cloud information acquired through the LiDAR sensor <NUM>-<NUM>, as described above.

As such, a computational load of the processor <NUM>-<NUM> may be reduced compared with in the case in which a position of the map using car <NUM>-<NUM> is determined using only information acquired through the LiDAR sensor <NUM>-<NUM>. That is, the position calculated through the GPS signal has a relatively high error range and, thus, the position of the map using car <NUM>-<NUM> may be primarily determined through the GPS signal, and map data within a preset range (e.g., a general error range for determining a position through the GPS signal) from the position determined through the GPS signal and information acquired through the LiDAR sensor <NUM>-<NUM> to accurately determine the position of the map using car <NUM>-<NUM>, thereby reducing a computational load of the processor <NUM>-<NUM> compared with a case in which entire map data received from the server <NUM> is matched.

The processor <NUM>-<NUM> may request the server <NUM> (illustrated in <FIG>) to transmit map data of a region corresponding to a preset range from the position of the map using car <NUM>-<NUM>, which is determined using the GPS signal. That is, the processor <NUM>-<NUM> may not request the server <NUM> to transmit map data of all regions that are stored in the server <NUM> but may request the server <NUM> to transmit only map data of a region corresponding to a predetermined range from the position determined through the GPS signal while the map using car <NUM>-<NUM> drives, thereby reducing a computational load of the processor <NUM>-<NUM>.

<FIG> is a block diagram illustrating components of a map using car <NUM>-<NUM> according to an embodiment of the present disclosure.

Referring to <FIG>, the map using car <NUM>-<NUM> includes a LiDAR sensor <NUM>-<NUM>, a processor <NUM>-<NUM>, a single-channel map <NUM>-<NUM> (illustrated in <FIG>), a communicator <NUM>-<NUM>, optionally a GPS receiver <NUM>-<NUM>, and optionally an ambient light sensor <NUM>-<NUM>. With regard to a description of <FIG>, a repeated description of the same components as the map using cars <NUM> and <NUM>-<NUM> illustrated in <FIG> and <FIG> will be omitted here.

The ambient light sensor <NUM>-<NUM> may detect ambient light of the map using car <NUM>-<NUM> and provide the detected ambient light information to the processor <NUM>-<NUM>. For example, the ambient light sensor <NUM>-<NUM> may detect intensity of sunlight that shines the map using car <NUM>-<NUM> and provide the intensity information of sunlight to the processor <NUM>-<NUM>. To this end, the ambient light sensor <NUM>-<NUM> may include various illuminance sensors without being limited thereto.

In addition, the processor <NUM>-<NUM> may determine a position of the map using car <NUM>-<NUM> through a GPS signal received through the GPS receiver <NUM>-<NUM> and determine a position of the sun based on a current time. In addition, although not illustrated, a moving direction of the map using car <NUM>-<NUM> may also be determined through an inertial sensor. Accordingly, the processor <NUM>-<NUM> may determine a position of the sun based on the position of the map using car <NUM>-<NUM>. For example, the processor <NUM>-<NUM> may determine a current position of the sun, that is, which side the sun is positioned among left, right, front, rear, and central directions of the map using car <NUM>-<NUM> and generate information on the determined position of the sun.

<FIG> is a diagram illustrating an example of a procedure in which the map using car <NUM>-<NUM> of <FIG> determines a position in a multichannel map <NUM>-<NUM> according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, the processor <NUM>-<NUM> may perform rasterization <NUM>-<NUM> on multichannel point cloud information <NUM>-<NUM> and apply various algorithms for removing reflected light to the rasterized 2D data for each channel rasterization <NUM>-<NUM> to perform reflected-light removal <NUM>-<NUM>.

In this case, the processor <NUM>-<NUM> may perform the reflected-light removal <NUM>-<NUM> using at least one of ambient light information <NUM>-<NUM> acquired through the ambient light sensor <NUM>-<NUM> and determine positional information <NUM>-<NUM> of the sun.

For example, when intensity of ambient light (e.g., sunlight) is high, the processor <NUM>-<NUM> may increase intensity of the reflected-light removal <NUM>-<NUM>, and when intensity of ambient light is low, the processor <NUM>-<NUM> may reduce intensity of the reflected-light removal <NUM>-<NUM>. However, the present disclosure is not limited to an example in which the reflected-light removal <NUM>-<NUM> is performed using ambient light information. For example, when intensity of sunlight is equal to or more than preset intensity, the reflected-light removal <NUM>-<NUM> may be performed, and the intensity of sunlight is less than preset intensity, the reflected-light removal <NUM>-<NUM> may not be performed.

For example, when the sun is positioned at front left of the map using car <NUM>-<NUM>, the possibility that reflected light is present at front left of the map using car <NUM>-<NUM> is high, and thus, the processor <NUM>-<NUM> may increase the intensity of the reflected-light removal <NUM>-<NUM> in a region corresponding to a front left side of the map using car <NUM>-<NUM> in the rasterized 2D data for each channel. In this manner, the processor <NUM>-<NUM> may apply information on a position of the sun and perform the reflected-light removal <NUM>-<NUM>.

Accordingly, the processor <NUM>-<NUM> may match the multichannel map <NUM>-<NUM> received through the server <NUM> through the communicator <NUM>-<NUM> and the rasterized multichannel 2D information from which reflected light is removed to determine in operation <NUM>-<NUM> a position of the map using car <NUM>-<NUM> in the multichannel map <NUM>-<NUM> and generate positional information <NUM>-<NUM>.

<FIG> is a diagram illustrating an example of a procedure in which the map using car <NUM>-<NUM> of <FIG> determines a position in a single-channel map <NUM>-<NUM> according to an embodiment of the present disclosure.

Referring to <FIG>, in order to determine a position of a map using car in the single-channel map <NUM>-<NUM>, the processor <NUM>-<NUM> first performs decolorization <NUM>-<NUM> on multichannel point cloud information <NUM>-<NUM> acquired through the LiDAR sensor <NUM>-<NUM> to synthesize single-channel point cloud information, performs rasterization <NUM>-<NUM> on the synthesized single-channel point cloud information, and then performs reflected-light removal <NUM>-<NUM> the rasterized single-channel 2D information.

In this case, adjustment of the reflected-light removal <NUM>-<NUM> based on at least one of ambient light information <NUM>-<NUM> and positional information <NUM>-<NUM> of the sun is the same as the above description of <FIG>, and the processor <NUM>-<NUM> matches the single-channel map <NUM>-<NUM> and the rasterized single-channel 2D information from which reflected light is removed to determine in operation <NUM>-<NUM> a localized position of the map using car <NUM>-<NUM> in the single-channel map <NUM>-<NUM> and generate positional information <NUM>-<NUM>.

It would be obvious to one of skill in the art that the components of the map using cars <NUM>, <NUM>-<NUM>, and <NUM>-<NUM> are combined in the various forms. For example, the LiDAR sensor <NUM>, the GPS receiver <NUM>-<NUM>, and the ambient light sensor <NUM>-<NUM> may be installed in the map using car <NUM> and the processor <NUM> or the communicator <NUM> may be included in a separate device (e.g., a notebook computer, a smart phone, a personal digital assistant (PDA), and a portable media player (PMP)) connected to the map using car <NUM>.

<FIG>, and <FIG> are flowcharts of a method of recognizing a position of a vehicle based on a map. With regard to a description of <FIG>, and <FIG>, the same description as the above description will not be repeatedly given here.

<FIG> is a flowchart of a method of recognizing a position of a vehicle based on a multichannel map according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, the map using car <NUM> may receive multichannel map data from the server <NUM> in operation S1510-<NUM>. In this case, the multichannel map may be a map that is generated by the server <NUM> or the map generating car <NUM> based on the multichannel point cloud information acquired by the map generating car <NUM> including a LiDAR sensor using the same frequency as the LiDAR sensor <NUM> included in the map using car <NUM>. For example, the multichannel map may be generated as described with reference to <FIG>, and in this case, the LiDAR sensor <NUM>-<NUM> of the map generating car <NUM> and the LiDAR sensor <NUM> of the map using car <NUM> may have the same laser frequency for each channel of the LiDAR sensor <NUM>.

The map using car <NUM> may acquire multichannel point cloud information through the LiDAR sensor <NUM> in operation S1520-<NUM>. Accordingly, the map using car <NUM> may match the received multichannel map data and the acquired multichannel point cloud information to determine a position of the map using car <NUM> in the multichannel map in operation S1530-<NUM>.

<FIG> is a flowchart of a method of recognizing a position of a vehicle based on a single-channel map according to an embodiment of the present disclosure.

Referring to <FIG>, the map using car <NUM> receives single-channel map data from the server <NUM> in operation S1510-<NUM>. In this case, the single-channel map may also be a map that is generated by the server <NUM> or the map generating car <NUM> based on the multichannel point cloud information acquired by the map generating car <NUM> including a LiDAR sensor using the same frequencies as those of the LiDAR sensor <NUM> of the map using car <NUM>. For example, the single-channel map may be generated as described with reference to <FIG>, and in this case, the LiDAR sensor <NUM>-<NUM> of the map generating car <NUM> and the LiDAR sensor <NUM> of the map using car <NUM> may have the same laser frequency for each channel of the LiDAR sensor <NUM>.

The map using car <NUM> acquires multichannel point cloud information on a ground on which the map using car <NUM> drives, through the LiDAR sensor <NUM> in operation S1520-<NUM> and multiplies the multichannel point cloud information by a weight for each weight to synthesize single-channel point cloud information so as to perform decolorization in operation S1530-<NUM>. In detail, the map using car <NUM> multiplies the point cloud information for each channel by a weight for each channel and sum the point cloud information for each channel, multiplied by a weight, to perform decolorization. In this case, the map using car <NUM> may calculate variance or standard deviation of reflectivity information included in the point cloud information for each channel and may multiply a channel with a greater calculated variance value or standard deviation by a greater weight, without being limited thereto.

Accordingly, the map using car <NUM> matches the single-channel map data and the single-channel point cloud information to determine a position of the map using car <NUM> in the single-channel map in operation S1540-<NUM>.

<FIG> is a flowchart of a method of recognizing a position of a vehicle based on a map according to an embodiment of the present disclosure.

Referring to <FIG>, the map using car <NUM> (illustrated in <FIG>) receives map data from the server <NUM> in operation S1610 and acquires multichannel point cloud information on a surrounding ground through the LiDAR sensor <NUM> in operation S1620. In this case, the map data is single-channel map data.

In addition, the map using car <NUM> may acquire ambient light information in operation S1630 and calculate positional information of the sun in operation S1640. In detail, the map using car <NUM> may acquire ambient light information such as intensity of sunlight through an ambient light sensor and calculate a position of the sun based on the GPS signal received from the GPS satellite of the sun based on GPS signal from the GPS signal received from the satellite and positional information of the sun based on a current time.

In response to multichannel map data being received, the map using car <NUM> may perform rasterization on the acquired multichannel point cloud information and generate 2D data for each channel in operation S1650 and apply various algorithms for reflected removal to the rasterized 2D data for each channel to pe4rform reflected-light removal in operation S1660. In this case, the map using car <NUM> may adjust a degree for removing reflected light based on at least one of ambient light information and a position of the sun.

In response to the single-channel map data being received, the map using car <NUM> first decolorizes the acquired multichannel point cloud information to synthesize single-channel point cloud information, rasterizes the synthesized single-channel point cloud information in operation S1650, and removes reflected light from the rasterized 2D data in operation S1660. In this case, needless to say, a degree for removing reflected light may be adjusted based on at least one of ambient light and positional information of the sun.

Accordingly, as described above, the map using car <NUM> may match information from which ambient light is removed and map data to determine a position of the map using car <NUM> in a map in operation S1670.

<FIG> is a flowchart of a case in which a map is generated and a vehicle recognizes a position based on the generated map in an autonomous driving system according to an unclaimed aspect of the present disclosure.

Referring to <FIG>, the map generating car <NUM> may acquire multichannel point cloud information on the ground through the multichannel LiDAR sensor in operation S1710 and transmit the same to the server <NUM> in operation S1720. Accordingly, the server <NUM> may generate a map using the multichannel point cloud information received from the map generating car <NUM> in operation S1730 and store the map. In this case, the server <NUM> may generate a multichannel map or perform decolorization on the multichannel point cloud information to generate a single-channel map.

Accordingly, in operation S1740, the server <NUM> may transmit the stored map to the map using car <NUM> according to a map data transmitting request from the map using car <NUM>.

The map using car <NUM> may acquire multichannel point cloud information through the multichannel LiDAR sensor <NUM> in operation S1750 and determine a position in the map received from the server <NUM> based on the acquired multichannel point cloud information in operation S1760.

According to the present disclosure, a detailed map with an error range less than a centimeter unit may be generated and a position of a vehicle may be accurately determined in the generated detailed map. Accordingly, autonomous driving of the vehicle may be easily achieved.

The methods of recognizing a position of a vehicle based on a map or the operations of the processors <NUM> and <NUM>-<NUM> and the single-channel map <NUM>-<NUM> of the map using car <NUM> according to the present disclosure may be generated and installed in a vehicle.

For example, a program to perform the methods of recognizing a position of a vehicle based on a map, including receiving map data from an external server, acquiring point cloud information for each channel on a surrounding ground of the vehicle using a multichannel LiDAR sensor, and determining a position of the vehicle in the map data based on the point cloud information for each channel may be stored in a non-transitory computer readable medium, and be provided.

The non-transitory computer readable medium is a medium that semi-permanently stores data and from which data is readable by a device, but not a medium that stores data for a short time, such as register, a cache, a memory, and the like. In detail, the aforementioned various applications or programs may be stored in the non-transitory computer readable medium, for example, a compact disc (CD), a digital versatile disc (DVD), a hard disk, a Blu-ray disc, a universal serial bus (USB), a memory card, a read only memory (ROM), and the like, and may be provided.

Claim 1:
A vehicle comprising:
a multichannel light detection and ranging, LiDAR, sensor (<NUM>, <NUM>) using a plurality of lasers with different frequencies for respective channels configured to acquire point cloud information for each of the channels on a surface of a surrounding ground of the vehicle;
a communicator (<NUM>, <NUM>, <NUM>) configured to communicate with an external server (<NUM>); and
a processor (<NUM>, <NUM>, <NUM>) configured to:
control the communicator (<NUM>, <NUM>, <NUM>) to receive map data which is a single-channel map data (<NUM>-<NUM>) obtained by synthesizing reflectivity information for each channel of the multichannel laser, from the external server (<NUM>),
multiply reflectivity information included in the point cloud information for each channel through the LiDAR sensor (<NUM>, <NUM>) by a weight for each channel,
synthesize the point cloud information into single-channel point cloud information by summing up the reflectivity information which is multiplied by the weight for each channel, and
determine a position of the vehicle in the map data by matching the synthesized single-channel point cloud information and the single-channel map data (<NUM>-<NUM>).