Information processing device and information processing method

Abstract

An information processing device (10) is provided with: a point group data acquiring unit (11) for acquiring point group data obtained by sensing around a moving body, or point group data stored in a point group database; a traffic information acquiring unit (12a) for acquiring traffic information around the moving body; a determining unit (13) for determining a specific region around the moving body on the basis of the traffic information; a compressing unit (14) for executing different compression control for first point group data corresponding to the specific region, among the point group data, and second point group data corresponding to a region other than the specific region; and an output unit (16) for outputting compressed data obtained by executing compression control with respect to the point group data.

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device and an information processing method for compressing point cloud data obtained by sensing the periphery of a moving object. [Background technology]

[0002] For a moving body such as an autonomous vehicle, point cloud data obtained by sensing the surroundings of the moving body can be transmitted to a server capable of performing more advanced processing than the moving body, and the server can perform advanced processing related to the movement of the moving body. When transmitting such point cloud data to the server, compressing the point cloud data enables the point cloud data to be transmitted to the server with low latency. For example, Patent Document 1 discloses a method for compressing point cloud data using encoding technology. Furthermore, for example, Patent Document 2 discloses a method for changing the compression method of point cloud data based on feature information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2019 / 103009 [Patent Document 2] Japanese Patent Application Publication No. 2018-116452 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the compression of point cloud data by the methods disclosed in Patent Documents 1 and 2 may not be suitable for the traveling of a mobile object.

[0005] Therefore, the present disclosure provides an information processing device and the like capable of compressing point cloud data suitable for the travel of a mobile body. [Means for solving the problem]

[0006] The information processing device according to the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database; a traffic information acquisition unit that acquires traffic information about the periphery of the mobile body; a determination unit that determines a specific area about the periphery of the mobile body based on the traffic information; a compression unit that performs different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area among the point cloud data; and an output unit that outputs compressed data obtained by performing the compression control on the point cloud data.

[0007] These comprehensive or specific aspects may be realized as a system, a method, an integrated circuit, a computer program, or a computer-readable recording medium such as a CD-ROM, or may be realized as any combination of a system, a method, an integrated circuit, a computer program, and a recording medium. [Effects of the Invention]

[0008] According to an information processing device or the like according to an aspect of the present disclosure, it is possible to compress point cloud data in a manner suitable for the travel of a moving body. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram illustrating an example of a mobile object and a remote processing server according to an embodiment. [Figure 2] FIG. 2 is a flowchart illustrating the operation of the information processing device according to the first embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the operation of the information processing device according to the first embodiment. [Figure 4] FIG. 4 is a flowchart illustrating the operation of the information processing device according to the second embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of the operation of the information processing device according to the second embodiment. [Figure 6] FIG. 6 is a diagram illustrating another example of the operation of the information processing device according to the second embodiment. [Figure 7] FIG. 7 is a flowchart illustrating the operation of the information processing device according to the third embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of the operation of the information processing device according to the third embodiment. [Figure 9] FIG. 9 is a flowchart illustrating the operation of the information processing device according to the fourth embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of the operation of the information processing device according to the fourth embodiment. [Figure 11] FIG. 11 is a flowchart illustrating the operation of the information processing device according to the fifth embodiment. [Figure 12] FIG. 12 is a diagram illustrating an example of the operation of the information processing device according to the fifth embodiment. [Figure 13] FIG. 13 is a diagram illustrating another example of the operation of the information processing device according to the fifth embodiment. [Figure 14] FIG. 14 is a flowchart illustrating the operation of the information processing device according to the sixth embodiment. [Figure 15] FIG. 15 is a diagram illustrating an example of the operation of the information processing device according to the sixth embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] An information processing device according to one embodiment of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database; a traffic information acquisition unit that acquires traffic information about the periphery of the mobile body; a determination unit that determines a specific area about the periphery of the mobile body based on the traffic information; a compression unit that performs different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area among the point cloud data; and an output unit that outputs compressed data obtained by performing the compression control on the point cloud data.

[0011] According to this, based on traffic information around the mobile body, it is possible to prevent point clouds from being deleted or reduce the amount of point cloud reduction for some areas around the mobile body, and to delete point clouds or increase the amount of point cloud reduction for other areas around the mobile body. In other words, it is possible to compress point cloud data in a way that is suitable for the traveling of the mobile body based on traffic information around the mobile body.

[0012] For example, the traffic information may include map information or congestion information.

[0013] This makes it possible to determine, based on map information or traffic congestion information, areas where point clouds should not be deleted or the amount of point cloud reduction can be reduced, or areas where point clouds should be deleted or the amount of point cloud reduction can be increased. Therefore, it is possible to control the amount of point clouds in areas important for the travel of a mobile object and other areas, thereby improving the processing efficiency of point cloud data while maintaining travel performance.

[0014] For example, the specific area may be an area where the possibility of an incident occurring is higher than in areas other than the specific area.

[0015] This makes it possible to prevent point clouds from being deleted or reduce the amount of point cloud reduction for specific areas where an incident is likely to occur, thereby improving the efficiency of point cloud data processing while maintaining driving safety and comfort.

[0016] An information processing device according to one embodiment of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database; a running state acquisition unit that acquires the running state of the moving body; a determination unit that determines a specific area within the periphery of the moving body based on the running state; a compression unit that performs different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area among the point cloud data; and an output unit that outputs compressed data obtained by performing the compression control on the point cloud data.

[0017] According to this, based on the traveling state of the mobile body, it is possible to prevent point clouds from being deleted or reduce the amount of point cloud reduction for some areas around the mobile body, and to delete point clouds or increase the amount of point cloud reduction for other areas around the mobile body. In other words, it is possible to compress point cloud data in a way that is suitable for the traveling state of the mobile body.

[0018] For example, the driving conditions may include a speed, a steering angle, or a self-position estimation accuracy.

[0019] This makes it possible to determine areas where point clouds should not be deleted or where the amount of point cloud reduction can be reduced, or areas where point clouds should be deleted or where the amount of point cloud reduction can be increased, depending on the speed, steering angle, or self-position estimation accuracy of the moving body. Therefore, it is possible to control the amount of point clouds in areas important for the traveling of the moving body and other areas, and improve the processing efficiency of point cloud data while maintaining traveling performance.

[0020] For example, the specific area may be an area within the stopping distance of the moving object.

[0021] This allows the point cloud to be prevented from being deleted or the amount of point cloud reduction to be reduced for the specific area within the stopping distance of the moving object, thereby improving the efficiency of processing point cloud data while maintaining driving safety and comfort.

[0022] An information processing device according to one embodiment of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database; a driving task acquisition unit that acquires a driving task of the moving body; a determination unit that determines a specific area within the periphery of the moving body based on the driving task; a compression unit that performs different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area among the point cloud data; and an output unit that outputs compressed data obtained by performing the compression control on the point cloud data.

[0023] According to this, based on the traveling task of the mobile body, it is possible to prevent the point cloud from being deleted or reduce the amount of point cloud reduction for some areas around the mobile body, and to delete the point cloud or increase the amount of point cloud reduction for other areas around the mobile body. In other words, it is possible to compress point cloud data in a way that is suitable for the traveling of the mobile body based on the traveling task of the mobile body.

[0024] For example, the driving task may include a task of turning right or left, changing lanes, or accelerating or decelerating.

[0025] This makes it possible to determine areas where point clouds should not be deleted or where the amount of point cloud reduction can be reduced, or areas where point clouds should be deleted or where the amount of point cloud reduction can be increased, depending on the task of turning right or left, changing lanes, or accelerating or decelerating the mobile body. Therefore, it is possible to control the amount of point clouds in areas important for the mobile body's travel and in other areas, thereby improving the processing efficiency of point cloud data while maintaining travel performance.

[0026] For example, the specific area may be an area in which the moving object travels by executing a travel task.

[0027] This makes it possible to prevent point clouds from being deleted or to reduce the amount of point cloud reduction for specific areas where a moving body travels by executing a travel task, thereby improving the efficiency of processing point cloud data while maintaining the safety and comfort of travel.

[0028] An information processing device according to one embodiment of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database; a surveillance information acquisition unit that acquires surveillance information from a monitor of the moving body or an object located in the periphery of the moving body; a determination unit that determines a specific area in the periphery of the moving body based on the surveillance information; a compression unit that performs different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area among the point cloud data; and an output unit that outputs compressed data obtained by performing the compression control on the point cloud data.

[0029] According to this, based on the monitoring information, it is possible to prevent point clouds from being deleted or reduce the amount of point cloud reduction for some areas around the moving object, and to delete point clouds or increase the amount of point cloud reduction for other areas around the moving object. In other words, it is possible to compress point cloud data in a way that is suitable for the movement of the moving object based on the monitoring information.

[0030] For example, the monitoring information may include information indicating the monitoring range of the monitor or information indicating the monitoring range of the object.

[0031] According to this, it is possible to determine areas where point clouds should not be deleted or the amount of point cloud reduction can be reduced, or areas where point clouds should be deleted or the amount of point cloud reduction can be increased, based on information indicating the monitoring range of the monitor or information indicating the monitoring range of the object. Therefore, it is possible to control the amount of point clouds in areas important to the mobile object being monitored while the mobile object is traveling and in other areas, thereby improving the processing efficiency of point cloud data while maintaining traveling performance.

[0032] For example, the specific area may be the monitoring range of the observer or an overlapping area between the monitoring range of the observer and the monitoring range of the object.

[0033] This makes it possible to prevent point cloud deletion or reduce the amount of point cloud reduction for the monitor's monitoring range, which is a specific area. Alternatively, it is possible to delete point clouds or increase the amount of point cloud reduction for the overlapping area between the monitor's monitoring range and the object's monitoring range, which is a specific area. Therefore, it is possible to improve the efficiency of processing point cloud data while maintaining driving safety and comfort.

[0034] For example, the compression unit may execute the compression control such that the first point cloud data and the second point cloud data are compressed or compressed by different amounts, or the amount of reduction of the point clouds is different.

[0035] In this way, by performing compression control so that the first point cloud data and the second point cloud data are compressed or not, or the amount of point cloud reduction is different, it is possible to compress the point cloud data in a manner suitable for the movement of a mobile body.

[0036] For example, the information processing device may further include an adding unit that adds additional information related to the compression control to the compressed data.

[0037] According to this, additional information relating to compression control is added to the compressed data, making it easier to handle the compressed data. For example, it is possible to determine a method for processing the compressed data based on the additional information.

[0038] An information processing method according to one embodiment of the present disclosure is an information processing method executed by a computer, and includes the steps of acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring traffic information about the periphery of the mobile body, determining a specific area about the periphery of the mobile body based on the traffic information, performing different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area, and outputting compressed data obtained by performing the compression control on the point cloud data.

[0039] This makes it possible to provide an information processing method that can compress point cloud data in a manner suitable for the travel of a mobile object.

[0040] An information processing method according to one embodiment of the present disclosure is an information processing method executed by a computer, and includes a process of acquiring point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database, acquiring the traveling state of the moving body, determining a specific area within the periphery of the moving body based on the traveling state, performing different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area, and outputting compressed data obtained by performing the compression control on the point cloud data.

[0041] This makes it possible to provide an information processing method that can compress point cloud data in a manner suitable for the travel of a mobile object.

[0042] An information processing method according to one embodiment of the present disclosure is an information processing method executed by a computer, and includes a process of acquiring point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database, acquiring a traveling task of the moving body, determining a specific area of ​​the periphery of the moving body based on the traveling task, performing different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area, and outputting compressed data obtained by performing the compression control on the point cloud data.

[0043] This makes it possible to provide an information processing method that can compress point cloud data in a manner suitable for the travel of a mobile object.

[0044] An information processing method according to one embodiment of the present disclosure is an information processing method executed by a computer, and includes the steps of acquiring point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database, acquiring surveillance information from a monitor of the moving body or an object located in the periphery of the moving body, determining a specific area in the periphery of the moving body based on the surveillance information, performing different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area, and outputting compressed data obtained by performing the compression control on the point cloud data.

[0045] This makes it possible to provide an information processing method that can compress point cloud data in a manner suitable for the travel of a mobile object.

[0046] An information processing device according to one aspect of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, a traffic information acquisition unit that acquires traffic information about the periphery of the mobile body, a determination unit that determines a compression mode for the point cloud data based on the traffic information, a compression unit that executes compression control of the point cloud data in the determined compression mode, and an output unit that outputs compressed data obtained by executing the compression control on the point cloud data.

[0047] This makes it possible to compress point cloud data in a manner suitable for the traveling of a mobile object based on traffic information around the mobile object.

[0048] An information processing device according to one aspect of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database, a driving state acquisition unit that acquires the driving state of the moving body, a determination unit that determines a compression mode of the point cloud data based on the driving state, a compression unit that executes compression control of the point cloud data in the determined compression mode, and an output unit that outputs compressed data obtained by executing the compression control on the point cloud data.

[0049] This makes it possible to compress point cloud data in a manner suitable for the traveling state of the mobile body based on the traveling state of the mobile body.

[0050] An information processing device according to one aspect of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database, a driving task acquisition unit that acquires a driving task of the moving body, a determination unit that determines a compression mode of the point cloud data based on the driving task, a compression unit that executes compression control of the point cloud data in the determined compression mode, and an output unit that outputs compressed data obtained by executing the compression control on the point cloud data.

[0051] This makes it possible to compress point cloud data in a manner suitable for the traveling of a mobile body based on the traveling task of the mobile body.

[0052] An information processing device according to one aspect of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database; a surveillance information acquisition unit that acquires surveillance information from a person monitoring the moving body or an object located in the periphery of the moving body; a determination unit that determines a compression mode for the point cloud data based on the surveillance information; a compression unit that executes compression control of the point cloud data in the determined compression mode; and an output unit that outputs compressed data obtained by executing the compression control on the point cloud data.

[0053] This makes it possible to compress point cloud data based on monitoring information in a manner suitable for the travel of a mobile object.

[0054] An information processing device according to one aspect of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database; a processing information acquisition unit that acquires processing information indicating the content of processing or the results of processing for autonomous driving of the mobile body; a determination unit that determines the compression mode of the point cloud data based on the processing information; a compression unit that executes compression control of the point cloud data in the determined compression mode; and an output unit that outputs compressed data obtained by executing the compression control on the point cloud data.

[0055] This enables compression of point cloud data suitable for the travel of a mobile body based on processing information related to the autonomous driving of the mobile body. Therefore, compression of point cloud data suitable for the autonomous driving process becomes possible. For example, for processes requiring high accuracy or precision, the point cloud can be not compressed or the degree of compression can be reduced, and for other processes, the point cloud can be compressed or the degree of compression can be increased. Furthermore, for example, the presence or absence of compression or the degree of compression can be controlled depending on the amount or type of processing results.

[0056] An information processing device according to one aspect of the present disclosure includes a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database, an operation information acquisition unit that acquires operation information of the moving body, a determination unit that determines a compression mode for the point cloud data based on the operation information, a compression unit that executes compression control of the point cloud data in the determined compression mode, and an output unit that outputs compressed data obtained by executing the compression control on the point cloud data.

[0057] This allows point cloud data to be compressed in a way that is suited to the running of the mobile body based on the operation information of the mobile body. Therefore, it is possible to compress point cloud data in a way that is suited to the running conditions of the mobile body. For example, in situations where there is a high possibility of a dangerous event such as an incident occurring in the mobile body, the point cloud data can be not compressed or the degree of compression can be reduced, and in other situations the point cloud data can be compressed or the degree of compression can be increased.

[0058] Hereinafter, the embodiments will be specifically described with reference to the drawings.

[0059] The embodiments described below are all comprehensive or specific examples, and the numerical values, shapes, materials, components, arrangement and connection of the components, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the present disclosure.

[0060] (Embodiment) [composition] FIG. 1 is a block diagram showing an example of a mobile object (specifically, an information processing device 10 mounted on the mobile object) and a remote processing server 100 according to an embodiment.

[0061] A mobile body is, for example, a vehicle that can travel automatically without the driver's operation. For example, such vehicles include those that travel completely independently, those that travel independently while being remotely monitored, and those that travel under remote control. Note that the mobile body may also be an autonomously mobile robot or unmanned aerial vehicle. The mobile body is equipped with sensors such as a camera, thermography, radar, LiDAR (Light Detection and Ranging), sonar, GPS (Global Positioning System), or IMU (Inertial Measurement Unit), and its movement is controlled using sensing data acquired by these sensors.

[0062] The remote processing server 100 can remotely control a mobile object by wirelessly communicating with the mobile object and processing point cloud data obtained by the mobile object. For example, the remote processing server 100 estimates the position of the mobile object and detects obstacles around the mobile object using the point cloud data obtained by the mobile object, and then remotely controls the mobile object using the results of the position estimation and the obstacle detection.

[0063] The mobile object is equipped with an information processing device 10. The information processing device 10 is a computer including a processor, a memory, a communication interface, etc. The memory is a read-only memory (ROM) and a random access memory (RAM), etc., and can store programs executed by the processor. The information processing device 10 includes a point cloud data acquisition unit 11, a traffic information acquisition unit 12a, a driving state acquisition unit 12b, a driving task acquisition unit 12c, a monitoring information acquisition unit 12d, a processing information acquisition unit 12e, an operation information acquisition unit 12f, a determination unit 13, a compression unit 14, an assignment unit 15, and an output unit 16. The point cloud data acquisition unit 11, the traffic information acquisition unit 12a, the driving state acquisition unit 12b, the driving task acquisition unit 12c, the monitoring information acquisition unit 12d, the processing information acquisition unit 12e, the operation information acquisition unit 12f, the determination unit 13, the compression unit 14, the assignment unit 15, and the output unit 16 are realized by a processor or the like that executes programs stored in the memory.

[0064] The point cloud data acquisition unit 11 acquires point cloud data obtained by a sensor such as a radar or LiDAR sensing the periphery of the mobile object. The point cloud data acquisition unit 11 may also acquire point cloud data obtained by a sensor installed on another mobile object or a roadside device, or point cloud data stored in a point cloud database. The point cloud data is coordinate data of each point, but is not limited to this. For example, the point cloud data may include other data such as the color of each point. The point cloud data may also be data such as polygons or meshes obtained by processing a point cloud.

[0065] The traffic information acquisition unit 12a acquires traffic information about the area surrounding the mobile object. The traffic information acquisition unit 12a will be described in detail later.

[0066] The traveling state acquisition unit 12b acquires the traveling state of the mobile object. Details of the traveling state acquisition unit 12b will be described later.

[0067] The travel task acquisition unit 12c acquires a travel task for the moving object. Details of the travel task acquisition unit 12c will be described later.

[0068] The monitoring information acquisition unit 12d acquires monitoring information from a person monitoring the moving object or an object located in the vicinity of the moving object. Details of the monitoring information acquisition unit 12d will be described later.

[0069] The processing information acquisition unit 12e acquires processing information indicating the content of processing or the results of processing for the automatic driving of the mobile object. Details of the processing information acquisition unit 12e will be described later.

[0070] The operation information acquisition unit 12f acquires operation information of the mobile object. Details of the operation information acquisition unit 12f will be described later.

[0071] The determination unit 13 determines the compression mode of the point cloud data acquired by the point cloud data acquisition unit 11 based on at least one of the traffic information, driving status, driving task, monitoring information, processing information, and operation information. For example, the determination unit 13 determines a specific area around the mobile object based on at least one of the traffic information, driving status, driving task, and monitoring information. Details of the determination unit 13 will be described later.

[0072] The compression unit 14 executes compression control of the point cloud data in the determined compression mode. For example, the compression unit 14 executes different compression controls for first point cloud data corresponding to a specific region and second point cloud data corresponding to a region other than the specific region, among the point cloud data acquired by the point cloud data acquisition unit 11. Details of the compression unit 14 will be described later.

[0073] The adding unit 15 adds additional information relating to compression control to the compressed data. Details of the additional information will be described later.

[0074] The output unit 16 outputs compressed data obtained by executing compression control on the point cloud data. For example, the output unit 16 outputs compressed data to which additional information has been added. For example, the output unit 16 transmits the compressed data to the remote processing server 100 via a communication interface or the like provided in the information processing device 10. Since the point cloud data has been compressed, the output unit 16 can transmit the point cloud data to the remote processing server 100 with low latency. Note that the compressed data may be used within the mobile body, and the output unit 16 may output the compressed data to a component that performs self-position estimation or obstacle detection in the mobile body. In this case, the processing load on the mobile body can be reduced.

[0075] The remote processing server 100 is a computer including a processor, a memory, a communication interface, etc. The memory is a ROM, a RAM, etc., and can store programs executed by the processor. The remote processing server 100 includes a monitoring information transmitting unit 101, a processing information transmitting unit 102, and a receiving unit 103. The monitoring information transmitting unit 101, the processing information transmitting unit 102, and the receiving unit 103 are realized by a processor, etc., that executes programs stored in the memory.

[0076] The monitoring information transmission unit 101 transmits monitoring information (for example, information indicating the monitoring range of a monitor of a mobile object) to the mobile object via a communication interface or the like that the remote processing server 100 has.

[0077] The processing information transmitting unit 102 transmits processing information (for example, processing details for automatic driving of the mobile body) to the mobile body via a communication interface or the like provided in the remote processing server 100. Specifically, the processing information transmitting unit 102 transmits processing details (for example, position estimation of the mobile body and obstacle detection around the mobile body) required for remotely controlling the mobile body to the mobile body.

[0078] The receiving unit 103 receives the compressed data from the mobile object via a communication interface or the like provided in the remote processing server 100. The compressed data received by the receiving unit 103 is used, for example, for estimating the position of the mobile object and detecting obstacles around the mobile object in the remote processing server 100, and is ultimately used for autonomous driving of the mobile object (e.g., an autonomous vehicle). The compressed data received by the receiving unit 103 may also be stored as a database. The processing information transmitting unit 102 may transmit to the mobile object information specifying the area or compression rate of the point cloud required when storing the point cloud data as a database.

[0079] The components constituting the remote processing server 100 may be distributed across multiple servers. Also, for example, a server may exist that stores point cloud data as a database separate from the remote processing server 100, and the server may transmit to the mobile object information that specifies the area or compression rate of the point cloud required for storing the point cloud data as a database.

[0080] Next, the operation of the information processing device 10 will be described with reference to first to sixth embodiments.

[0081] [Example 1] First, the first embodiment will be described with reference to FIGS.

[0082] FIG. 2 is a flowchart illustrating the operation of the information processing device 10 according to the first embodiment.

[0083] First, the point cloud data acquisition unit 11 acquires point cloud data obtained by sensing the periphery of a moving object or point cloud data stored in a point cloud database (step S11).

[0084] Next, the traffic information acquisition unit 12a acquires traffic information around the mobile object (step S12). For example, the traffic information includes map information or congestion information. For example, the traffic information acquisition unit 12a acquires the map information or congestion information from an external server or a car navigation system mounted on the mobile object. For example, the map information includes road context around the mobile object (information about objects or signs on the road, road types such as roadways or sidewalks, road surface conditions or road shapes such as intersections, etc.) or a risk map, etc. For example, the congestion information includes information about areas with a high density of vehicles or people, etc.

[0085] Next, the determination unit 13 determines the compression mode of the point cloud data based on the traffic information acquired by the traffic information acquisition unit 12a (step S13). For example, the determination unit 13 determines a specific area around the moving object based on the traffic information acquired by the traffic information acquisition unit 12a. For example, determining the specific area is an example of determining the compression mode.

[0086] Next, the compression unit 14 executes compression control of the point cloud data in the determined compression mode (step S14). For example, the compression unit 14 executes different compression controls for the first point cloud data corresponding to a specific region and the second point cloud data corresponding to a region other than the specific region, among the point cloud data acquired by the point cloud data acquisition unit 11. For example, the compression unit 14 executes compression control such that the first point cloud data and the second point cloud data are compressed or not, or the amount of point cloud reduction is different. For example, executing different compression controls for the first point cloud data and the second point cloud data is an example of executing compression control of point cloud data in the determined compression mode.

[0087] Here, a specific example of the operations of the determination unit 13 and the compression unit 14 in the first embodiment will be described with reference to FIG.

[0088] Fig. 3 is a diagram illustrating an example of the operation of the information processing device 10 according to the first embodiment. Fig. 3 shows an intersection in a country where left-hand traffic is practiced (for example, Japan).

[0089] For example, it is assumed that the traffic information acquisition unit 12a acquires from map information, as traffic information around a moving object (an object indicated by a triangle in FIG. 3), that the moving object is about to enter an intersection.

[0090] Based on such traffic information, the determination unit 13 determines an area 20a as a specific area around the mobile object. The specific area may be specified on a map or by a geofence. The specific area 20a is, for example, an area where the possibility of an incident occurring is higher than in areas other than the specific area, and specifically, an intersection and a lane entering the intersection. An intersection is an area where an incident is likely to occur because mobile objects (e.g., vehicles) pass each other, and a lane entering the intersection is also an area where an incident is likely to occur because it is a lane where vehicles enter such an intersection.

[0091] The compression unit 14 performs different compression controls for the first point cloud data corresponding to the specific area 20a and the second point cloud data corresponding to areas other than the area 20a (such as the lane where vehicles exit an intersection and areas outside the road). Specifically, the compression unit 14 does not compress the first point cloud data, but compresses the second point cloud data (e.g., deletes the point cloud). Alternatively, the compression unit 14 reduces the amount of point cloud in the second point cloud data more than the amount of point cloud in the first point cloud data. That is, the compression unit 14 does not delete or reduces the amount of point cloud in the first point cloud data corresponding to the specific area where an incident is likely to occur, and deletes or increases the amount of point cloud in the second point cloud data corresponding to the area other than the specific area where an incident is unlikely to occur. Note that hereinafter, not deleting a point cloud or reducing the amount of point cloud reduction is referred to as lowering the compression rate, and deleting a point cloud or increasing the amount of point cloud reduction is referred to as increasing the compression rate. For example, the compression rate for the first point cloud data is low because the first point cloud data is used for obstacle detection for collision avoidance, etc. For example, the compression rate for the second point cloud data is high because the second point cloud data is used for position estimation of a moving object, which does not require a high-density point cloud.

[0092] The determination unit 13 may determine an area where congestion occurs as a specific area around the mobile object based on traffic information (for example, congestion information).

[0093] In this case, the compression unit 14 performs different compression controls for the first point cloud data corresponding to the area where congestion occurs and the second point cloud data corresponding to the area other than the area where congestion occurs. Specifically, the compression unit 14 does not compress the point cloud for the first point cloud data, but compresses (e.g., deletes) the point cloud for the second point cloud data. Alternatively, the compression unit 14 reduces the point cloud of the second point cloud data by a larger amount than the point cloud of the first point cloud data. That is, the compression unit 14 does not delete or reduces the point cloud for the first point cloud data corresponding to a specific area where congestion occurs, and deletes or increases the point cloud for the second point cloud data corresponding to an area other than the specific area. For example, because the first point cloud data is used for obstacle detection to avoid collisions with many obstacles due to congestion, the compression rate for the first point cloud data is low. For example, the second point cloud data is used for estimating the position of a moving object, which does not require a high-density point cloud, and therefore the compression rate for the second point cloud data is set high.

[0094] The assigning unit 15 assigns additional information to compressed data obtained by executing compression control on the point cloud data (step S15). For example, the assigning unit 15 assigns information indicating which area of ​​the point cloud has been deleted or the amount of reduction increased to the compressed data as additional information. This makes it possible to recognize which area of ​​the point cloud data has been processed when handling the compressed data.

[0095] Then, the output unit 16 outputs the compressed data (for example, the compressed data to which the additional information has been added) (step S16).

[0096] As described above, based on traffic information around the mobile body, it is possible to prevent point clouds from being deleted or reduce the amount of point cloud reduction for some areas around the mobile body (for example, a specific area), and to delete point clouds or increase the amount of point cloud reduction for other areas around the mobile body (for example, areas other than the specific area). In other words, it is possible to compress point cloud data in a way that is suitable for the traveling of the mobile body based on traffic information around the mobile body.

[0097] In the first embodiment, the information processing device 10 does not necessarily have to include the traveling state acquisition unit 12b, the traveling task acquisition unit 12c, the monitoring information acquisition unit 12d, the processing information acquisition unit 12e, and the operation information acquisition unit 12f.

[0098] [Example 2] Next, a second embodiment will be described with reference to FIGS.

[0099] FIG. 4 is a flowchart illustrating the operation of the information processing device 10 according to the second embodiment.

[0100] First, the point cloud data acquisition unit 11 acquires point cloud data obtained by sensing the periphery of a moving object or point cloud data stored in a point cloud database (step S21).

[0101] Next, the traveling state acquisition unit 12b acquires the traveling state of the mobile object (step S22). For example, the traveling state includes the speed, steering angle, or self-position estimation accuracy of the mobile object. Furthermore, for example, the traveling state includes the type of driver of the mobile object. For example, the traveling state acquisition unit 12b acquires the speed, steering angle, self-position estimation accuracy, or type of driver of the mobile object from various ECUs (Electronic Control Units) etc. equipped in the mobile object.

[0102] Next, the determination unit 13 determines a compression mode of the point cloud data based on the traveling state acquired by the traveling state acquisition unit 12b (step S23). For example, the determination unit 13 determines a specific area around the moving object based on the traveling state acquired by the traveling state acquisition unit 12b. For example, determining the specific area is an example of determining a compression mode.

[0103] Next, the compression unit 14 executes compression control of the point cloud data in the determined compression manner (step S24). For example, the compression unit 14 executes different compression controls for the first point cloud data corresponding to a specific region and the second point cloud data corresponding to a region other than the specific region, among the point cloud data acquired by the point cloud data acquisition unit 11. For example, the compression unit 14 executes compression control such that the first point cloud data and the second point cloud data are compressed or not, or the amount of point cloud reduction is different. For example, executing different compression controls for the first point cloud data and the second point cloud data is an example of executing compression control of point cloud data in the determined compression manner.

[0104] Here, a specific example of the operations of the determination unit 13 and the compression unit 14 in the second embodiment will be described with reference to FIG.

[0105] Fig. 5 is a diagram illustrating an example of the operation of the information processing device 10 according to the second embodiment. Fig. 5 shows a two-lane road in a country where left-hand traffic is practiced (for example, Japan).

[0106] For example, it is assumed that the traveling state acquisition unit 12b acquires the speed and steering angle of a moving body (an object indicated by a triangle in FIG. 5) as the traveling state of the moving body.

[0107] The determination unit 13 determines the area 20b as a specific area around the moving object based on such traveling conditions. The specific area may be specified on a map or by a geofence. The specific area 20b is, for example, an area within a stopping distance, which is a distance at which the moving object can stop when stopping. For example, the area 20b can be determined based on the speed of the moving object, the moving direction of the moving object based on the steering angle of the moving object, the deceleration of the moving object, system delay, etc. The reason the area 20b is fan-shaped is because there is a possibility that the moving object will deviate from its current moving direction.

[0108] The compression unit 14 performs different compression controls for the first point cloud data corresponding to the specific region 20b and the second point cloud data corresponding to a region other than the region 20b (e.g., a region where a moving object does not enter when stopping). Specifically, the compression unit 14 does not compress the point cloud of the first point cloud data, but compresses the point cloud of the second point cloud data (e.g., deletes the point cloud). Alternatively, the compression unit 14 reduces the point cloud of the second point cloud data by a larger amount than the first point cloud data. That is, the compression unit 14 does not delete or reduces the point cloud of the first point cloud data corresponding to a specific region that is a region within the stopping distance of the moving object, and deletes or reduces the point cloud of the second point cloud data corresponding to a region other than the specific region where a moving object does not enter. For example, the compression rate for the first point cloud data is low because the first point cloud data is used for obstacle detection for collision avoidance, etc. For example, the second point cloud data is used for estimating the position of a moving object, which does not require a high-density point cloud, and therefore the compression rate for the second point cloud data is set high.

[0109] Another specific example of the operations of the determination unit 13 and the compression unit 14 in the second embodiment will be described with reference to FIG.

[0110] FIG. 6 is a diagram illustrating another example of the operation of the information processing device 10 according to the second embodiment.

[0111] For example, it is assumed that the traveling state acquisition unit 12b acquires the type of the driving entity of the moving body as the traveling state of the moving body. For example, when the traveling mode of the moving body is a completely autonomous mode, the type of the driving entity of the moving body is a moving body, when the traveling mode of the moving body is a remote monitoring mode, the type of the driving entity of the moving body is a remotely monitored moving body, and when the traveling mode of the moving body is a remote control mode, the type of the driving entity of the moving body is a remote operator.

[0112] The determination unit 13 determines the compression mode of the point cloud data based on such driving conditions. For example, when the driving mode of the mobile body is a fully autonomous mode (i.e., when the type of driver is a mobile body), the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 2 m. For example, when the driving mode of the mobile body is a remote monitoring mode (i.e., when the type of driver is a mobile body with remote monitoring), the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 1 m. For example, when the driving mode of the mobile body is a remote control mode (i.e., when the type of driver is a remote monitor), the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 0.5 m.

[0113] The compression unit 14 executes compression control of the point cloud data in the determined compression mode. For example, when the traveling mode of the mobile object is a fully autonomous mode, the compression unit 14 executes compression control of the point cloud data in a compression mode in which the downsampling voxel size is 2 m. For example, when the traveling mode of the mobile object is a fully autonomous mode, the remote processing server 100 basically does not control the movement of the mobile object and does not require a high-density point cloud, so the compression rate is high. For example, when the traveling mode of the mobile object is a remote monitoring mode, the compression unit 14 executes compression control of the point cloud data in a compression mode in which the downsampling voxel size is 1 m. For example, when the traveling mode of the mobile object is a remote monitoring mode, the remote processing server 100 controls the movement of the mobile object (e.g., sudden braking) in an emergency, so that point cloud data with a certain degree of density is required, so the compression rate is lowered to a certain degree. For example, when the traveling mode of the mobile object is a remote operation mode, the compression unit 14 executes compression control of the point cloud data in a compression mode in which the downsampling voxel size is 0.5 m. For example, when a moving body is in remote control mode, the remote processing server 100 (e.g., a remote monitor who manages the remote processing server 100) remotely controls the movement of the moving body, so a high-density point cloud is required, and therefore the compression rate is low.

[0114] The assigning unit 15 assigns additional information to compressed data obtained by executing compression control on the point cloud data (step S25). For example, the assigning unit 15 assigns information indicating which area of ​​the point cloud has been deleted or the amount of reduction has been increased to the compressed data as additional information. This makes it possible to recognize which area of ​​the point cloud data has been processed when the compressed data is handled. Furthermore, for example, the assigning unit 15 assigns information indicating the compression rate of the point cloud data to the compressed data as additional information. This makes it possible to recognize the extent to which the point cloud data has been compressed and processed when the compressed data is handled.

[0115] Then, the output unit 16 outputs the compressed data (for example, the compressed data to which the additional information has been added) (step S26).

[0116] As described above, based on the traveling state of the mobile body, it is possible to prevent the point cloud from being deleted or reduce the amount of point cloud reduction for some areas (e.g., specific areas) around the mobile body, and to delete the point cloud or increase the amount of point cloud reduction for other areas (e.g., areas other than the specific areas) around the mobile body. In other words, it is possible to compress point cloud data in a way that is suitable for the traveling of the mobile body based on the traveling state of the mobile body.

[0117] In the second embodiment, the information processing device 10 does not necessarily have to include the traffic information acquisition unit 12a, the driving task acquisition unit 12c, the monitoring information acquisition unit 12d, the processing information acquisition unit 12e, and the operation information acquisition unit 12f.

[0118] [Example 3] Next, a third embodiment will be described with reference to FIGS.

[0119] FIG. 7 is a flowchart illustrating the operation of the information processing device 10 according to the third embodiment.

[0120] First, the point cloud data acquisition unit 11 acquires point cloud data obtained by sensing the periphery of a moving object or point cloud data stored in a point cloud database (step S31).

[0121] Next, the traveling task acquisition unit 12c acquires a traveling task of the mobile object (step S32). For example, the traveling task includes a task of turning right or left, changing lanes, or accelerating or decelerating the mobile object. For example, the traveling task acquisition unit 12c acquires the task of turning right or left, changing lanes, or accelerating or decelerating the mobile object from various ECUs, etc., provided in the mobile object.

[0122] Next, the determination unit 13 determines a compression mode of the point cloud data based on the traveling task acquired by the traveling task acquisition unit 12c (step S33). For example, the determination unit 13 determines a specific area around the moving object based on the traveling task acquired by the traveling task acquisition unit 12c. For example, determining the specific area is an example of determining a compression mode.

[0123] Next, the compression unit 14 executes compression control of the point cloud data in the determined compression manner (step S34). For example, the compression unit 14 executes different compression controls for the first point cloud data corresponding to a specific region and the second point cloud data corresponding to a region other than the specific region, among the point cloud data acquired by the point cloud data acquisition unit 11. For example, the compression unit 14 executes compression control such that the first point cloud data and the second point cloud data are compressed or compressed by different amounts, or the amount of point cloud reduction is different. For example, executing different compression controls for the first point cloud data and the second point cloud data is an example of executing compression control of point cloud data in the determined compression manner.

[0124] Here, a specific example of the operations of the determination unit 13 and the compression unit 14 in the third embodiment will be described with reference to FIG.

[0125] Fig. 8 is a diagram illustrating an example of the operation of the information processing device 10 according to the third embodiment. Fig. 8 shows a two-lane road in a country where driving on the left side is adopted (for example, Japan).

[0126] For example, it is assumed that the traveling task acquisition unit 12c acquires, as a traveling task of a moving object (an object indicated by a triangle in FIG. 8), that the moving object is about to change lanes into an overtaking lane.

[0127] The determination unit 13 determines the area 20c as a specific area around the mobile object based on the travel task. The specific area may be specified on a map or by a geofence. The specific area 20c is, for example, an area in which the mobile object travels by executing the travel task, and specifically, in this case, is a passing lane.

[0128] The compression unit 14 performs different compression controls for the first point cloud data corresponding to the specific region 20c and the second point cloud data corresponding to a region other than the region 20c (e.g., the lane on which the mobile object is currently traveling). Specifically, the compression unit 14 does not compress the point cloud of the first point cloud data, but compresses the point cloud of the second point cloud data (e.g., deletes the point cloud). Alternatively, the compression unit 14 reduces the point cloud of the second point cloud data by a larger amount than the first point cloud data. That is, the compression unit 14 does not delete or reduces the point cloud of the first point cloud data corresponding to the specific region through which the mobile object travels by performing a traveling task, and deletes or increases the point cloud of the second point cloud data corresponding to a region other than the specific region. For example, because the first point cloud data is used for obstacle detection for rear-end collision avoidance, the compression rate for the first point cloud data is low. For example, the second point cloud data is used for estimating the position of a moving object, which does not require a high-density point cloud, and therefore the compression rate for the second point cloud data is set high.

[0129] When the traveling task acquisition unit 12c acquires that the traveling task is to accelerate or decelerate to a specific instructed speed, the specific area may be determined according to the instructed speed. For example, a smaller instructed speed allows the moving body to stop in a shorter stopping distance, so a small specific area may be determined in front of the moving body.

[0130] Furthermore, when the traveling task acquisition unit 12c acquires a highly urgent task such as emergency braking as a traveling task, it is necessary to check the detailed situation around the moving object, so the area around the moving object may be determined as the specific area. In other words, the compression rate of the point cloud for the area around the moving object may be reduced.

[0131] The assigning unit 15 assigns additional information to the compressed data obtained by executing compression control on the point cloud data (step S35). For example, the assigning unit 15 assigns information indicating which area of ​​the point cloud has been deleted or the amount of reduction increased to the compressed data as additional information. This makes it possible to recognize which area of ​​the point cloud data has been processed when handling the compressed data.

[0132] Then, the output unit 16 outputs the compressed data (for example, the compressed data to which the additional information has been added) (step S36).

[0133] As described above, based on the traveling task of the mobile body, it is possible to prevent the point cloud from being deleted or reduce the amount of point cloud reduction for some areas (e.g., specific areas) around the mobile body, and to delete the point cloud or increase the amount of point cloud reduction for other areas (e.g., areas other than the specific areas) around the mobile body. In other words, it is possible to compress point cloud data in a way that is suitable for the traveling of the mobile body based on the traveling task of the mobile body.

[0134] In the third embodiment, the information processing device 10 does not necessarily have to include the traffic information acquisition unit 12a, the traveling state acquisition unit 12b, the monitoring information acquisition unit 12d, the processing information acquisition unit 12e, and the operation information acquisition unit 12f.

[0135] [Example 4] Next, a fourth embodiment will be described with reference to FIGS.

[0136] FIG. 9 is a flowchart illustrating the operation of the information processing device 10 according to the fourth embodiment.

[0137] First, the point cloud data acquisition unit 11 acquires point cloud data obtained by sensing the periphery of a moving object or point cloud data stored in a point cloud database (step S41).

[0138] Next, the monitoring information acquisition unit 12d acquires monitoring information from a monitor of the mobile body or an object located in the vicinity of the mobile body (step S42). The object located in the vicinity of the mobile body is a vehicle or a roadside device traveling around the mobile body. For example, the monitoring information includes information indicating the monitoring range of the monitor of the mobile body or information indicating the monitoring range of the object. For example, the monitoring range of the monitor is the sensing range of a sensor mounted on the mobile body. This is because monitoring by the monitor is performed through a sensor mounted on the mobile body. For example, the monitoring information acquisition unit 12d acquires information indicating the monitoring range of the monitor of the mobile body from an external server (e.g., the remote processing server 100) via a communication interface or the like provided in the information processing device 10. Furthermore, for example, the monitoring information acquisition unit 12d acquires information indicating the monitoring range of the object from the object via a communication interface or the like provided in the information processing device 10. Note that the monitoring information acquisition unit 12d may acquire information indicating the monitoring range of the object from an external server via a communication interface or the like provided in the information processing device 10.

[0139] Next, the determination unit 13 determines a compression mode of the point cloud data based on the monitoring information acquired by the monitoring information acquisition unit 12d (step S43). For example, the determination unit 13 determines a specific area around the moving object based on the monitoring information acquired by the monitoring information acquisition unit 12d. For example, determining the specific area is an example of determining a compression mode.

[0140] Next, the compression unit 14 executes compression control of the point cloud data in the determined compression mode (step S44). For example, the compression unit 14 executes different compression controls for the first point cloud data corresponding to a specific region and the second point cloud data corresponding to a region other than the specific region, among the point cloud data acquired by the point cloud data acquisition unit 11. For example, the compression unit 14 executes compression control such that the first point cloud data and the second point cloud data are compressed or compressed by different amounts, or the amount of point cloud reduction is different. For example, executing different compression controls for the first point cloud data and the second point cloud data is an example of executing compression control of point cloud data in the determined compression mode.

[0141] Here, a specific example of the operations of the determination unit 13 and the compression unit 14 in the fourth embodiment will be described with reference to FIG.

[0142] Fig. 10 is a diagram illustrating an example of the operation of the information processing device 10 according to the fourth embodiment. Fig. 10 illustrates a two-lane road in a country where left-hand traffic is practiced (for example, Japan).

[0143] For example, the surveillance information acquisition unit 12d acquires an area 20d as the surveillance range of a surveillance person of a moving body (an object indicated by a triangle on the lower left side in Figure 10), and acquires an area 30a as the surveillance range of an object located around the moving body (for example, another moving body traveling in the oncoming lane, an object indicated by a triangle on the upper right side in Figure 10).

[0144] Based on such monitoring information, the determination unit 13 determines the area 20e as a specific area around the mobile object. The specific area may be specified on a map or by a geofence. The specific area 20e is, for example, an overlapping area between the monitoring range of the monitor (i.e., area 20d) and the monitoring range of the object (i.e., area 30a). The area 20e may be calculated by the mobile object itself from the area 20d and area 30a, or may be notified by the remote processing server 100.

[0145] The compression unit 14 performs different compression controls for the first point cloud data corresponding to the specific region 20e and the second point cloud data corresponding to regions other than region 20e. Specifically, the compression unit 14 compresses (e.g., deletes) the point clouds of the first point cloud data, but does not compress the point clouds of the second point cloud data. Alternatively, the compression unit 14 reduces the point cloud of the first point cloud data by a larger amount than the point cloud of the second point cloud data. That is, the compression unit 14 deletes or reduces the point cloud amount of the first point cloud data corresponding to the specific region, which is an overlapping region between the monitoring range of the monitor and the monitoring range of the object, and does not delete or reduces the point cloud amount of the second point cloud data corresponding to regions other than the specific region. For example, the point cloud data in the specific region can achieve sufficient density by integrating point cloud data from a moving object (i.e., the first point cloud data) with point cloud data from other moving objects, so the compression rate for the first point cloud data is increased. For example, since the second point cloud data is point cloud data in an area that does not overlap with the monitoring range of other moving bodies, the compression rate for the second point cloud data is set low.

[0146] The determining unit 13 may determine the area 20d as a specific area in the vicinity of the moving object based on the monitoring information. In this case, the specific area 20d is, for example, a monitoring range of a monitoring person.

[0147] In this case, the compression unit 14 performs different compression controls for the first point cloud data corresponding to the specific area 20d and the second point cloud data corresponding to areas other than area 20d. Specifically, the compression unit 14 does not compress the point clouds of the first point cloud data, but compresses (e.g., deletes) the point clouds of the second point cloud data. Alternatively, the compression unit 14 reduces the point clouds of the second point cloud data by a larger amount than the first point cloud data. That is, the compression unit 14 does not delete or reduces the point cloud reduction amount for the first point cloud data corresponding to the specific area that is the monitoring range of the observer, and deletes or increases the point cloud reduction amount for the second point cloud data corresponding to areas other than the specific area. For example, because the first point cloud data is used to monitor the observer, the compression rate for the first point cloud data is low. For example, because the second point cloud data is not used to monitor the observer, the compression rate for the second point cloud data is high.

[0148] The assigning unit 15 assigns additional information to compressed data obtained by executing compression control on the point cloud data (step S45). For example, the assigning unit 15 assigns information indicating which area of ​​the point cloud has been deleted or the amount of reduction has been increased to the compressed data as additional information. This makes it possible to recognize which area of ​​the point cloud data has been processed when the compressed data is handled. Furthermore, for example, the assigning unit 15 assigns information indicating the compression rate of the point cloud data to the compressed data as additional information. This makes it possible to recognize the extent to which the point cloud data has been compressed and processed when the compressed data is handled.

[0149] Then, the output unit 16 outputs the compressed data (for example, the compressed data to which the additional information has been added) (step S46).

[0150] As described above, based on the monitoring information, it is possible to prevent point clouds from being deleted or reduce the amount of point cloud reduction for some areas (e.g., specific areas) around the mobile object, and to delete point clouds or increase the amount of point cloud reduction for other areas (e.g., areas other than the specific areas) around the mobile object. In other words, it is possible to compress point cloud data in a way that is suitable for the travel of the mobile object based on the monitoring information.

[0151] In the fourth embodiment, the information processing device 10 does not necessarily have to include the traffic information acquisition unit 12a, the traveling state acquisition unit 12b, the traveling task acquisition unit 12c, the processing information acquisition unit 12e, and the operation information acquisition unit 12f.

[0152] [Example 5] Next, a fifth embodiment will be described with reference to FIGS.

[0153] FIG. 11 is a flowchart illustrating the operation of the information processing device 10 according to the fifth embodiment.

[0154] First, the point cloud data acquisition unit 11 acquires point cloud data obtained by sensing the periphery of a moving object or point cloud data stored in a point cloud database (step S51).

[0155] Next, the processing information acquisition unit 12e acquires processing information indicating the content of processing or the result of processing for the autonomous driving of the mobile body (step S52). For example, the processing information acquisition unit 12e acquires the content of processing for the autonomous driving of the mobile body from an external server (for example, the remote processing server 100) via a communication interface or the like provided in the information processing device 10. Furthermore, for example, the processing information acquisition unit 12e acquires the result of processing for the autonomous driving of the mobile body from a processing unit (not shown) that performs the processing, which the mobile body has.

[0156] Next, the determination unit 13 determines the compression mode of the point cloud data based on the processing information acquired by the processing information acquisition unit 12e (step S53).

[0157] Next, the compression unit 14 executes compression control of the point cloud data in the determined compression mode (step S54).

[0158] Here, a specific example of the operations of the determination unit 13 and the compression unit 14 in the fifth embodiment will be described with reference to FIG.

[0159] FIG. 12 is a diagram illustrating an example of the operation of the information processing device 10 according to the fifth embodiment.

[0160] For example, it is assumed that the processing information acquisition unit 12e acquires, as processing information, the content of processing for automatic driving of a mobile body (for example, self-location estimation or obstacle monitoring) from the remote processing server 100. In other words, the remote processing server 100 is attempting to perform self-location estimation or obstacle monitoring for automatic driving of a mobile body.

[0161] The determination unit 13 determines the compression mode of the point cloud data based on such processing information. For example, when the processing content for the autonomous driving of the mobile body is self-position estimation, the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 2m. For example, when the processing content for the autonomous driving of the mobile body is obstacle monitoring, the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 1m.

[0162] The compression unit 14 executes compression control of the point cloud data in the determined compression mode. For example, when the processing for autonomous driving of a mobile body is self-localization, the compression unit 14 executes compression control of the point cloud data in a compression mode in which the downsampling voxel size is 2m. Since self-localization does not require a high-density point cloud, the compression rate is set high. For example, when the processing for autonomous driving of a mobile body is obstacle monitoring, the compression unit 14 executes compression control of the point cloud data in a compression mode in which the downsampling voxel size is 1m. Since obstacle monitoring requires a high-density point cloud, the compression rate is set low.

[0163] In addition, when the processing for automatic driving of a mobile body involves obstacle monitoring, the compression unit 14 may not compress the point cloud on the road or may reduce the amount of point cloud reduction, and may compress the point cloud other than the point cloud on the road or may increase the amount of point cloud reduction.

[0164] The processing information may also include information indicating a compression method (e.g., an instruction for a compression rate or an instruction for an area where the compression rate is to be lowered (or raised)). In this case, the determination unit 13 may determine a compression mode (e.g., a compression rate or a specific area) based on such processing information, and the compression unit 14 may execute compression control of the point cloud data in the determined compression mode.

[0165] Next, another specific example of the operation of the determination unit 13 and the compression unit 14 in the fifth embodiment will be described with reference to FIG.

[0166] FIG. 13 is a diagram illustrating another example of the operation of the information processing device 10 according to the fifth embodiment.

[0167] For example, it is assumed that the processing information acquisition unit 12e has acquired, as the processing information, the results of processing for automatic driving of a mobile body (for example, the results of obstacle monitoring).

[0168] The determination unit 13 determines the compression mode of the point cloud data based on such processing information. For example, if the result of the processing for automatic driving of the mobile body is that there are few obstacles, the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 2m. For example, if the result of the processing for automatic driving of the mobile body is that there are many obstacles, the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 1m.

[0169] The compression unit 14 executes compression control of the point cloud data in the determined compression mode. For example, if the result of the processing for the autonomous driving of the mobile body is that there are few obstacles, the compression unit 14 executes compression control of the point cloud data in a compression mode in which the downsampling voxel size is set to 2m. If there are few obstacles, a high-density point cloud is not required, so the compression rate is set high. For example, if the result of the processing for the autonomous driving of the mobile body is that there are many obstacles, the compression unit 14 executes compression control of the point cloud data in a compression mode in which the downsampling voxel size is set to 1m. If there are many obstacles, a high-density point cloud is required to detect many obstacles, so the compression rate is set low.

[0170] The assigning unit 15 assigns additional information to compressed data obtained by executing compression control on the point cloud data (step S55). For example, the assigning unit 15 assigns information indicating the compression rate of the point cloud data to the compressed data as additional information. This makes it possible to recognize the extent to which the point cloud data has been compressed and processed when handling the compressed data.

[0171] Then, the output unit 16 outputs the compressed data (for example, the compressed data to which the additional information has been added) (step S56).

[0172] As described above, it is possible to compress point cloud data in a manner suitable for the traveling of a mobile object based on the processing information of the mobile object.

[0173] In the fifth embodiment, the information processing device 10 does not necessarily have to include the traffic information acquiring unit 12a, the traveling state acquiring unit 12b, the traveling task acquiring unit 12c, the monitoring information acquiring unit 12d, and the operation information acquiring unit 12f.

[0174] [Example 6] Next, a sixth embodiment will be described with reference to FIGS.

[0175] FIG. 14 is a flowchart illustrating the operation of the information processing device 10 according to the sixth embodiment.

[0176] First, the point cloud data acquisition unit 11 acquires point cloud data obtained by sensing the periphery of a moving object or point cloud data stored in a point cloud database (step S61).

[0177] Next, the operation information acquiring unit 12f acquires operation information of the mobile object (step S62). For example, the operation information acquiring unit 12f acquires operation information of the mobile object from various ECUs, etc., provided in the mobile object. For example, the operation information includes an operation schedule of the mobile object. Furthermore, for example, if the mobile object is a vehicle that transports passengers, the operation information includes an operation status such as stopping, passengers getting on and off, and passengers being transported.

[0178] Next, the determination unit 13 determines the compression mode of the point cloud data based on the operation information acquired by the operation information acquisition unit 12f (step S63).

[0179] Next, the compression unit 14 executes compression control of the point cloud data in the determined compression mode (step S64).

[0180] Here, a specific example of the operations of the determination unit 13 and the compression unit 14 in the sixth embodiment will be described with reference to FIG.

[0181] FIG. 15 is a diagram illustrating an example of the operation of the information processing device 10 according to the sixth embodiment.

[0182] For example, it is assumed that the operation information acquisition unit 12f has acquired the operation schedule of the mobile object (for example, whether the mobile object is currently in operation or outside of operation hours) as the operation information.

[0183] The determination unit 13 determines the compression mode of the point cloud data based on such operation information. For example, when the moving object is currently in operation, the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 0.5 m. For example, when the moving object is currently out of operation, the determination unit 13 sets the downsampling voxel size as the compression rate of the point cloud data to 3 m.

[0184] The compression unit 14 executes compression control of the point cloud data in the determined compression mode. For example, when the mobile object is currently in operation, the compression unit 14 executes compression control of the point cloud data in a compression mode that sets the downsampling voxel size to 0.5 m. Point cloud data during operation is likely to be used in later data analysis, and a high-density point cloud is likely to be required, so the compression rate is low. For example, when the mobile object is currently out of operation, the compression unit 14 executes compression control of the point cloud data in a compression mode that sets the downsampling voxel size to 3 m. Point cloud data outside of operation is unlikely to be used in later data analysis, and a high-density point cloud is likely to be required, so the compression rate is high.

[0185] The compression unit 14 may lower the compression rate of the point cloud data when the mobile object is transporting passengers, and may increase the compression rate of the point cloud data when the mobile object is parked.

[0186] The assigning unit 15 assigns additional information to compressed data obtained by executing compression control on the point cloud data (step S65). For example, the assigning unit 15 assigns information indicating the compression rate of the point cloud data to the compressed data as additional information. This makes it possible to recognize the extent to which the point cloud data has been compressed and processed when handling the compressed data.

[0187] Then, the output unit 16 outputs the compressed data (for example, the compressed data to which the additional information has been added) (step S66).

[0188] As described above, it is possible to compress point cloud data suitable for the running of a mobile body based on the operation information of the mobile body.

[0189] In the sixth embodiment, the information processing device 10 does not necessarily have to include the traffic information acquiring unit 12a, the traveling state acquiring unit 12b, the traveling task acquiring unit 12c, the monitoring information acquiring unit 12d, and the processing information acquiring unit 12e.

[0190] (Other embodiments) While the information processing device 10 according to one or more aspects of the present disclosure has been described above based on the embodiments, the present disclosure is not limited to these embodiments. As long as they do not deviate from the spirit of the present disclosure, various modifications conceivable by a person skilled in the art to each embodiment and configurations constructed by combining components of different embodiments may also be included within the scope of one or more aspects of the present disclosure.

[0191] For example, the specific region may be a region where the point cloud is not compressed or where the amount of reduction of the point cloud is small, or a region where the point cloud is compressed or where the amount of reduction of the point cloud is large.

[0192] For example, if a mobile object is equipped with two or more sensors such as radar or LiDAR, the compression rate of the overlapping sensing ranges of the sensors may be increased. However, if one of the sensors fails, the compression rate of the overlapping sensing range may be decreased.

[0193] For example, if a communication delay occurs between the remote processing server 100 and the mobile body, the compression rate may be increased. This reduces the data size of the compressed data to be transmitted, making it possible to transmit compressed data between the remote processing server 100 and the mobile body even if a communication delay occurs between the remote processing server 100 and the mobile body.

[0194] For example, the compression rate may be increased for point clouds that are not often used for obstacle detection, such as those behind, below, and above the moving body.

[0195] For example, the compression rate may be increased for a group of points that are at a certain distance or more from the center of the moving object.

[0196] For example, the compression rate may be increased for point clouds in areas that do not need to be monitored depending on the behavior of the moving object. For example, when the moving object is moving straight, the compression rate may be increased for point clouds in areas on the left and right of the moving object. For example, when the moving object changes lanes to the right, the compression rate may be increased for point clouds in areas on the left of the moving object.

[0197] For example, the compression rate of the point cloud may be changed depending on the recording during the movement of the mobile object. For example, when moving through a location where self-location estimation was unstable, the compression rate of the point cloud for that location may be lowered.

[0198] For example, the compression rate may be low for point clouds in locations where the likelihood of the moving body detecting an obstacle is low, while the compression rate may be high for point clouds in locations where the likelihood of the moving body detecting an obstacle is high.

[0199] For example, the compression rate of the point cloud may be changed depending on the time period that the moving object is traveling in. For example, the compression rate of the point cloud may be lowered during times of heavy traffic.

[0200] For example, the additional information may include information indicating the positional relationship of a specific region with respect to the position of the moving body (for example, in front of or behind the moving body). For example, the additional information may include coordinate information of the point cloud of the compressed data (such as latitude, longitude, or relative coordinates centered on the moving body). For example, the additional information may include information indicating the type of processing recommended for the point cloud of the compressed data (such as self-localization or obstacle estimation). For example, the additional information may include information regarding a point cloud corresponding to a region of the point cloud of the compressed data recommended for use in processing (such as an uncompressed region). For example, the additional information may include information indicating the traveling state of the moving body when the compressed data is transmitted (such as a right turn, left turn, or straight ahead). For example, the additional information may include information indicating the state of the moving body when the compressed data is transmitted (such as a sensor failure state). For example, the additional information may include information regarding the compression mode (such as the voxel size used when downsampling, the region from which the point cloud was deleted, the reason for compression, or how many frames of information were overlaid). For example, the additional information may include velocity information of the point cloud of the compressed data. For example, the additional information may include image information from a camera at the time the point cloud of the compressed data was acquired and information about the camera (such as the coordinates, type, or viewing angle of the camera). For example, the additional information may include a point cloud surrounding a specific area. In other words, to identify the specific area, a specific area on the point cloud data may be surrounded by a point cloud that is the additional information.

[0201] For example, although an example in which the information processing device 10 is mounted on a mobile body has been described, the information processing device 10 may be provided in a server (such as the remote processing server 100). In this case, the data size of the point cloud data handled by the server can be reduced, thereby reducing the processing load on the server.

[0202] For example, the compression rate may be controlled in accordance with the communication bandwidth between the remote processing server 100 and the mobile body. For example, the compression rate may be controlled so that the narrower the communication bandwidth, the higher the compression rate. Conversely, the wider the communication bandwidth, the lower the compression rate. This ensures that the data size of the data to be transmitted is appropriate for the communication bandwidth between the remote processing server 100 and the mobile body, thereby suppressing data loss due to communication delays or communication collisions.

[0203] For example, the compression rate may be controlled according to the driving mode of the mobile object. For example, when the driving mode is a fully autonomous mode, the compression rate may be set higher than in other driving modes. When the driving mode is a remote monitoring mode, the compression rate may be set lower than in the fully autonomous mode and higher than in the remote control mode. When the driving mode is a remote control mode, the compression rate may be set lower than in other driving modes.

[0204] The present disclosure can be realized not only as the information processing device 10 but also as an information processing method including steps (processing) performed by each component of the information processing device 10.

[0205] For example, an information processing method executed by a computer includes a process of acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring traffic information around the mobile body, determining a specific area around the mobile body based on the traffic information, performing different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area, and outputting compressed data obtained by performing compression control on the point cloud data.

[0206] For example, the information processing method may be executed by a computer, and may include a process of acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring the traveling state of the mobile body, determining a specific area within the periphery of the mobile body based on the traveling state, performing different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area, and outputting compressed data obtained by performing compression control on the point cloud data.

[0207] For example, an information processing method executed by a computer includes a process of acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring a traveling task of the mobile body, determining a specific area within the periphery of the mobile body based on the traveling task, performing different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area, and outputting compressed data obtained by performing compression control on the point cloud data.

[0208] For example, an information processing method executed by a computer includes a process of acquiring point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database, acquiring surveillance information from a monitor of the moving body or an object located in the periphery of the moving body, determining a specific area in the periphery of the moving body based on the surveillance information, performing different compression controls on first point cloud data corresponding to the specific area and second point cloud data corresponding to an area other than the specific area, and outputting compressed data obtained by performing compression control on the point cloud data.

[0209] For example, the information processing method may be executed by a computer, and may include processing for acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring traffic information about the periphery of the mobile body, determining a compression mode for the point cloud data based on the traffic information, executing compression control of the point cloud data in the determined compression mode, and outputting compressed data obtained by executing compression control on the point cloud data.

[0210] For example, the information processing method may be executed by a computer, and may include processing for acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring the traveling state of the mobile body, determining the compression mode of the point cloud data based on the traveling state, executing compression control of the point cloud data in the determined compression mode, and outputting compressed data obtained by executing compression control on the point cloud data.

[0211] For example, the information processing method may be executed by a computer, and may include processing for acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring a driving task of the mobile body, determining a compression mode for the point cloud data based on the driving task, executing compression control of the point cloud data in the determined compression mode, and outputting compressed data obtained by executing compression control on the point cloud data.

[0212] For example, the information processing method may be executed by a computer, and may include processing for acquiring point cloud data obtained by sensing the periphery of a moving body or point cloud data stored in a point cloud database, acquiring monitoring information from a monitor of the moving body or an object located in the periphery of the moving body, determining a compression mode for the point cloud data based on the monitoring information, executing compression control of the point cloud data in the determined compression mode, and outputting compressed data obtained by executing compression control on the point cloud data.

[0213] For example, the information processing method may be executed by a computer, and may include processing for acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring processing information indicating the content of processing for automatic driving of the mobile body or the results of the processing, determining the compression mode of the point cloud data based on the processing information, executing compression control of the point cloud data in the determined compression mode, and outputting compressed data obtained by executing compression control on the point cloud data.

[0214] For example, the information processing method may be executed by a computer, and may include processing for acquiring point cloud data obtained by sensing the periphery of a mobile body or point cloud data stored in a point cloud database, acquiring operation information of the mobile body, determining a compression mode for the point cloud data based on the operation information, executing compression control of the point cloud data in the determined compression mode, and outputting compressed data obtained by executing compression control on the point cloud data.

[0215] For example, the present disclosure can be realized as a program for causing a processor to execute steps included in an information processing method. Furthermore, the present disclosure can be realized as a non-transitory computer-readable recording medium, such as a CD-ROM, on which the program is recorded.

[0216] For example, when the present disclosure is realized as a program (software), each step is performed by running the program using hardware resources such as a computer's CPU, memory, input / output circuits, etc. In other words, each step is performed by the CPU acquiring data from memory or input / output circuits, etc., performing calculations on the data, and outputting the calculation results to memory or input / output circuits, etc.

[0217] In the above embodiment, each component included in the information processing device 10 may be configured with dedicated hardware, or may be realized by executing a software program suitable for each component. Each component may also be realized by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0218] Some or all of the functions of the information processing device 10 according to the above-described embodiment are typically realized as an LSI, which is an integrated circuit. These may be individually integrated into single chips, or some or all of them may be integrated into a single chip. Furthermore, the integrated circuit is not limited to an LSI, and may be realized by a dedicated circuit or a general-purpose processor. An FPGA (Field Programmable Gate Array) that can be programmed after LSI manufacture, or a reconfigurable processor that can reconfigure the connections and settings of circuit cells within an LSI, may also be used.

[0219] Furthermore, various modifications made to the embodiments of the present disclosure within the scope that would occur to a person skilled in the art are also included in the present disclosure, as long as they do not deviate from the gist of the present disclosure. [Industrial Applicability]

[0220] The present disclosure can be applied to remote control systems for mobile objects such as self-driving cars. [Explanation of symbols]

[0221] 10. Information processing equipment 11 Point cloud data acquisition section 12a Traffic information acquisition section 12b Driving state acquisition unit 12c Driving task acquisition unit 12d Monitoring information acquisition unit 12e Processing information acquisition unit 12f Operation information acquisition section 13 Decision Section 14 Compression section 15 Granting Department 16 Output section 20a, 20b, 20c, 20d, 20e, 30a area 100 Remote Processing Server 101 Monitoring information transmission unit 102 Processing information transmission unit 103 Receiving unit

Claims

1. a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of the moving object or point cloud data stored in a point cloud database; a traffic information acquisition unit that acquires traffic information around the mobile object; a determination unit that determines a specific area around the mobile object based on the traffic information; a compression unit that performs different compression controls on first point cloud data corresponding to the specific region and second point cloud data corresponding to a region other than the specific region, among the point cloud data; an output unit that outputs compressed data obtained by executing the compression control on the point cloud data, The specific area is an area where the possibility of an incident occurring is higher than in areas other than the specific area. Information processing device.

2. The traffic information includes map information or congestion information. The information processing device according to claim 1 .

3. a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of the moving object or point cloud data stored in a point cloud database; a running state acquisition unit that acquires a running state of the moving body; a determination unit that determines a specific area around the moving object based on the running state; a compression unit that performs different compression controls on first point cloud data corresponding to the specific region and second point cloud data corresponding to a region other than the specific region, among the point cloud data; an output unit that outputs compressed data obtained by executing the compression control on the point cloud data. Information processing device.

4. The driving state includes a speed, a steering angle, or a self-position estimation accuracy. The information processing device according to claim 3 .

5. The specific area is an area within the stopping distance of the moving object.

5. The information processing device according to claim 3 or 4.

6. a point cloud data acquisition unit that acquires point cloud data obtained by sensing the periphery of the moving object or point cloud data stored in a point cloud database; a travel task acquisition unit that acquires a travel task of the moving object; a determination unit that determines a specific area around the moving object based on the traveling task; a compression unit that performs different compression controls on first point cloud data corresponding to the specific region and second point cloud data corresponding to a region other than the specific region, among the point cloud data; an output unit that outputs compressed data obtained by executing the compression control on the point cloud data. Information processing device.

7. The driving task includes a task of turning right or left, changing lanes, or accelerating or decelerating. The information processing device according to claim 6 .

8. The specific area is an area in which the moving body travels by executing a travel task.

8. The information processing device according to claim 6 or 7.

9. An information processing method executed by a computer, Acquire point cloud data obtained by sensing the periphery of the moving object or point cloud data stored in a point cloud database; Obtaining traffic information around the mobile object; determining a specific area around the mobile object based on the traffic information; performing different compression controls on first point cloud data corresponding to the specific region and second point cloud data corresponding to a region other than the specific region, among the point cloud data; outputting compressed data obtained by executing the compression control on the point cloud data; The specific area is an area where the possibility of an incident occurring is higher than in areas other than the specific area. Information processing methods.

10. An information processing method executed by a computer, Acquire point cloud data obtained by sensing the periphery of the moving object or point cloud data stored in a point cloud database; Acquire the running state of the moving body; determining a specific area around the moving object based on the running state; performing different compression controls on first point cloud data corresponding to the specific region and second point cloud data corresponding to a region other than the specific region, among the point cloud data; Outputting compressed data obtained by executing the compression control on the point cloud data Information processing methods.

11. An information processing method executed by a computer, Acquire point cloud data obtained by sensing the periphery of the moving object or point cloud data stored in a point cloud database; obtaining a travel task of the moving object; determining a specific area around the moving object based on the running task; performing different compression controls on first point cloud data corresponding to the specific region and second point cloud data corresponding to a region other than the specific region, among the point cloud data; Outputting compressed data obtained by executing the compression control on the point cloud data Information processing methods.

Images