System and method

Abstract

To generate a 3D map used for detecting a detection target or monitoring a monitoring target area.SOLUTION: A system comprises: a placement unit where a LiDAR is placed; a motor to rotate the placement unit; and an acquisition unit for acquiring a measurement result measured by the LiDAR while the placement unit is rotated using the motor so as to generate a 3D map. A computer-implemented method includes an acquisition step of acquiring a measurement result measured by a LiDAR while a motor to rotate a placement unit where the LiDAR is placed is used to rotate the placement unit so as to generate a 3D map.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a system and method. [Background technology]

[0002] Patent Document 1 describes a LiDAR (Light Detection and Ranging) device mounted on a vehicle. [Prior art document] [Patent documents] [Patent Document 1] JP 2019-156261 A Summary of the Invention

[0003] According to an embodiment of the present invention, there is provided a system. The system may include a mounting unit that mounts a LiDAR. The system may include a motor that rotates the mounting unit. The system may include an acquisition unit that acquires measurement results measured by the LiDAR while rotating the mounting unit using the motor to generate a 3D map.

[0004] The system may further comprise a generator configured to generate the 3D map based on the measurement results.

[0005] Any of the systems may further include a detection unit that detects a predetermined detection target based on the 3D map generated by the generation unit.

[0006] In any of the systems, the detection unit may detect the detection target by aligning the 3D map generated by the generation unit with a 3D map in a state in which the detection target is not included, thereby matching each of a plurality of first points in the 3D map generated by the generation unit with each of a plurality of second points in the 3D map in a state in which the detection target is not included, and detecting a target point where the distance in map space from a first point of the plurality of first points to a second point associated with the first point is longer than a predetermined first distance threshold.

[0007] In any of the systems, the detection unit may classify, for each of the multiple detected target points, a single target point as a core point if the number of other target points whose distances to the single target point in the map space are shorter than a predetermined second distance threshold is greater than a predetermined first target point number threshold, classify the single target point as a boundary point if the number of the other target points is less than the first target point number threshold and the core point is included in the other target points, classify the single target point as a noise point if the number of the other target points is less than the first target point number threshold and the core point is not included in the other target points, cluster the detected multiple target points, and determine that the detection target is present in an area corresponding to the single cluster if the number of target points clustered to belong to the single cluster as a result of the clustering is greater than a predetermined second target point number threshold.

[0008] In any of the systems described above, the detection section may determine whether or not the detection target is present in the area corresponding to the one cluster, further based on a shape and a size of the one cluster.

[0009] Any of the systems may further include a division unit that divides a horizontal plane in the map space of the 3D map generated by the generation unit into a plurality of grid cells, the grid cell size of which is determined based on the size of the detection target, and the detection unit may determine that the detection target is present in an area corresponding to a grid cell among the plurality of grid cells in which the number of target points existing in the vertical direction in the map space is greater than a predetermined third target point number threshold.

[0010] Any of the systems may further include a zone setting unit that sets a zone specified by a user of the system for the 3D map generated by the generation unit.

[0011] Any of the systems may further include a detection unit that detects, based on the 3D map generated by the generation unit, that a predetermined detection target is present in an area corresponding to the zone set by the zone setting unit, and a monitoring unit that monitors the area corresponding to the zone set by the zone setting unit based on a detection result by the detection unit.

[0012] Any of the systems may further comprise the LiDAR.

[0013] According to one embodiment of the invention, there is provided a computer-implemented method, which may comprise acquiring measurements taken by a LiDAR while rotating a mount carrying the LiDAR using a motor that rotates the mount to generate a 3D map.

[0014] The above summary of the invention does not list all of the necessary features of the present invention. Also, subcombinations of these features may also be inventions. [Brief description of the drawings]

[0015] [Figure 1] An example of a system 10 is shown diagrammatically. [Diagram 2] 1 illustrates an example of a hardware configuration of the rotating LiDAR 100. [Diagram 3] 1 illustrates an example of a functional configuration of the rotating LiDAR 100. [Figure 4] 2 illustrates an example of a functional configuration of an information processing device 200. [Diagram 5] FIG. 11 is an explanatory diagram for explaining an example in which the information processing device 200 aligns a detection 3D map with a background 3D map. [Figure 6] FIG. 2 is an explanatory diagram for explaining an example in which the information processing device 200 detects a detection target in a normal mode. [Figure 7] FIG. 11 is an explanatory diagram for explaining an example in which the information processing device 200 detects a detection target in a dense mode. [Figure 8] FIG. 2 is an explanatory diagram for explaining an example of a processing flow of the system 10. [Figure 9] 11 is an explanatory diagram for explaining another example of the processing flow of the system 10. FIG. [Figure 10] 11 is an explanatory diagram for explaining another example of the processing flow of the system 10. FIG. [Figure 11] An example of a hardware configuration of a computer 1200 functioning as the rotating LiDAR 100 or the information processing device 200 is illustrated roughly. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0016] The present invention will be described below through embodiments of the invention, but the following embodiments do not limit the invention according to the claims. In addition, not all of the combinations of features described in the embodiments are necessarily essential to the solution of the invention.

[0017] FIG 1 illustrates an example of a system 10. The system 10 may include a rotating LiDAR 100. Although FIG 1 illustrates an example in which the system 10 includes one rotating LiDAR 100, the system 10 may include a plurality of rotating LiDARs 100. The system 10 may include an information processing device 200. The system 10 may include a sensor 300.

[0018] The system 10 provides, for example, a service of generating a 3D map using the rotating LiDAR 100. The system 10 provides, for example, a service of detecting a detection target using the rotating LiDAR 100. The system 10 provides, for example, a service of monitoring a predetermined monitoring target area using the rotating LiDAR 100. The system 10 provides, for example, a service of supporting the movement of a vehicle or a robot.

[0019] The rotating LiDAR 100 outputs a laser beam from the LiDAR while rotating the LiDAR, and measures the distance between the output position of the laser beam and the point at which the laser beam is reflected based on the reflected beam of the laser beam. For example, when the time from the output time of the laser beam to the reception time of the reflected beam of the laser beam reflected at the point is t [s] and the propagation speed of the laser beam is c [m / s], the rotating LiDAR 100 measures the distance x [m] between the output position of the laser beam and the point at which the laser beam is reflected as x [m] = 1 / 2 × t [s] × c [m / s].

[0020] The rotating LiDAR 100 scans the surroundings of the rotating LiDAR 100, for example, by rotating the LiDAR by a predetermined angle. The rotating LiDAR 100 scans the surroundings of the rotating LiDAR 100, for example, by rotating the LiDAR 360 degrees.

[0021] The rotating LiDAR 100 may scan the surroundings of the rotating LiDAR 100 by rotating the LiDAR more than 360 degrees. For example, the rotating LiDAR 100 scans the surroundings of the rotating LiDAR 100 by rotating the LiDAR 720 degrees. For example, the rotating LiDAR 100 scans the surroundings of the rotating LiDAR 100 by rotating the LiDAR 1080 degrees.

[0022] The rotating LiDAR 100 may scan the surroundings of the rotating LiDAR 100 by rotating the LiDAR less than 360 degrees. The rotating LiDAR 100 scans the surroundings of the rotating LiDAR 100, for example, by rotating the LiDAR 180 degrees. The rotating LiDAR 100 scans the surroundings of the rotating LiDAR 100, for example, by rotating the LiDAR 90 degrees.

[0023] The rotating LiDAR 100 scans the periphery of the rotating LiDAR 100, for example, by continuously outputting laser light from the LiDAR while the LiDAR is rotating. The rotating LiDAR 100 scans the periphery of the rotating LiDAR 100, for example, by outputting laser light from the LiDAR at predetermined angular intervals while the LiDAR is rotating. The rotating LiDAR 100 may scan the periphery of the rotating LiDAR 100 by outputting laser light from the LiDAR at predetermined time intervals while the LiDAR is rotating.

[0024] The rotating LiDAR 100 acquires measurement results to generate a 3D map, for example. The 3D map includes, for example, a 3D map in a state where the detection target is not included (sometimes referred to as a "background 3D map"). The 3D map includes, for example, a 3D map in a state where the detection target may be included (sometimes referred to as a "detection 3D map").

[0025] The 3D map may be used, for example, to detect a target object. The 3D map may be used, for example, to monitor a target area. The 3D map may be used to guide a vehicle or a robot.

[0026] The rotating LiDAR 100 communicates with, for example, an information processing device 200. The rotating LiDAR 100 communicates with the information processing device 200 via, for example, a network 20.

[0027] The network 20 includes, for example, a LAN (Local Area Network). The LAN includes, for example, a wireless LAN conforming to a standard such as Wi-Fi (registered trademark). The LAN may include a wired LAN conforming to a standard such as Ethernet (registered trademark). The network 20 may include a mobile communication network. The network 20 may include the Internet.

[0028] The rotating LiDAR 100, for example, transmits the measurement results to the information processing device 200. The rotating LiDAR 100 transmits the measurement results to the information processing device 200 while scanning the surroundings, for example. The rotating LiDAR 100 may transmit the measurement results to the information processing device 200 in a lump in response to completion of scanning the surroundings.

[0029] The information processing device 200 executes various types of processing. For example, the information processing device 200 executes a process of generating a 3D map.

[0030] The information processing device 200 generates a 3D map based on, for example, the measurement results received from the rotating LiDAR 100 via the network 20. The information processing device 200 may generate the 3D map based further on sensor data detected by the sensor 300 from the sensor 300 via the network 20.

[0031] The sensor 300 includes, for example, a camera. The sensor 300 includes, for example, a distance measurement sensor. The sensor 300 may include any other sensor.

[0032] The information processing device 200 generates, for example, a background 3D map. For example, the information processing device 200 receives a measurement result from the rotating LiDAR 100 when the rotating LiDAR 100 rotates the LiDAR 110 by 360 degrees, and creates a background 3D map based on the measurement result.

[0033] The information processing device 200 generates, for example, a detection 3D map. For example, the information processing device 200 receives a measurement result from the rotating LiDAR 100 every time the rotating LiDAR 100 rotates the LiDAR 110 by 180 degrees, combines the measurement results from the rotating LiDAR 100 every time the rotating LiDAR 100 rotates the LiDAR 110 by 180 degrees, and creates a detection 3D map based on the combined measurement results.

[0034] The information processing device 200 executes, for example, a detection process of a detection target. For example, the information processing device 200 aligns the detection 3D map and the background 3D map to locate a plurality of points P d and multiple points P of the background 3D map b Next, the information processing device 200 associates each of the points P d For each of the points P d From the point P d The point P associated with b The information processing device 200 determines whether the distance d in the map space to the point P is longer than a predetermined distance threshold δ. d On the other hand, the information processing device 200 maintains a point P d from the detected 3D map. That is, the information processing device 200 deletes points P d are filtered from the detected 3D map.

[0035] The information processing device 200 detects the detection target based on the filtered detection 3D map. The information processing device 200 detects the detection target by, for example, executing an algorithm for detecting the detection target. The algorithm for detecting the detection target will be described in detail later.

[0036] The information processing device 200 executes, for example, a monitoring process for a monitoring target area. The information processing device 200 monitors, for example, the monitoring target area by monitoring whether or not a position of a detection target detected by a detection process is included in the monitoring target area.

[0037] Conventional fixed LiDARs have a limited irradiation angle of laser light that can be output within a range of approximately 30 degrees to approximately 50 degrees. Therefore, when using a fixed LiDAR to simultaneously perform a process for generating a 3D map of a wide-angle area such as an omnidirectional area and a process for detecting a detection target in the wide-angle area or a process for monitoring the wide-angle area, multiple fixed LiDARs are indispensable. In addition, when using a single fixed LiDAR that can cover a wide-angle area to generate a high-density 3D map, a laser light source with a propagation distance of approximately 20 to 30 m must be used.

[0038] In contrast, according to the system 10 of this embodiment, the rotating LiDAR 100 is used to obtain the measurement results required for generating a 3D map while rotating the LiDAR with a motor. As a result, the system 10 of this embodiment can simultaneously perform the generation process of a 3D map of a wide-angle area and the detection process of a detection target in the wide-angle area or the monitoring process of the wide-angle area using only one LiDAR. Furthermore, since the rotating LiDAR 100 can change the output position of the laser light output from the LiDAR by rotating the LiDAR with a motor, it is possible to generate a 3D map with the same level of density as when a high-density 3D map is generated using a fixed LiDAR by using a laser light source with a propagation distance of about 200 m. As a result, the system 10 of this embodiment can realize the detection of a detection target located farther away with high accuracy and the monitoring of a monitoring target area located farther away with high accuracy.

[0039] 2 is a schematic diagram illustrating an example of a hardware configuration of the rotating LiDAR 100. The rotating LiDAR 100 may include a LiDAR 110, a mounting unit 120, and a housing 150.

[0040] The LiDAR 110 has an output unit 115 that outputs laser light. The output unit 115 includes, for example, a laser light source that outputs laser light with a wavelength in the infrared region. The output unit 115 includes, for example, a laser light source that outputs laser light with a wavelength in the visible light region. The output unit 115 may include a laser light source that outputs laser light with a wavelength in the ultraviolet region.

[0041] The output unit 115 includes, for example, 16 laser light sources. The output unit 115 includes, for example, 32 laser light sources. The output unit 115 includes, for example, 64 laser light sources. The output unit 115 includes, for example, 128 laser light sources.

[0042] The mounting unit 120 mounts the LiDAR 110. The mounting unit 120 rotates with the LiDAR 110 mounted thereon, for example, by power energy from a motor. The mounting unit 120 is connected to the motor via, for example, a slip ring.

[0043] The mounting unit 120 rotates, for example, around the z-axis of the xyz coordinate system shown in Fig. 2. The mounting unit 120 rotates, for example, clockwise around the z-axis. The mounting unit 120 rotates, for example, counterclockwise around the z-axis.

[0044] The mounting unit 120 rotates at a constant angular velocity around the z-axis, for example. The rotating LiDAR 100 can specify the rotation angle of the mounting unit 120 when the output unit 115 outputs the laser light without using an inertial motion unit (IMU) by rotating the mounting unit 120 at a constant angular velocity using a motor.

[0045] The housing 150 houses various devices. The housing 150 houses, for example, a motor. The housing 150 houses, for example, a circuit board that functions as a communication unit of the rotating LiDAR 100. The housing 150 houses, for example, a minicomputer board that functions as a control unit that controls the LiDAR 110 and the motor of the rotating LiDAR 100.

[0046] 3 is a schematic diagram showing an example of a functional configuration of the rotating LiDAR 100. The rotating LiDAR 100 includes a control unit 102, an acquisition unit 104, a generation unit 106, a communication unit 108, a LiDAR 110, a mounting unit 120, and a motor 130. It is not essential that the rotating LiDAR 100 includes all of these components.

[0047] The control unit 102 controls the controlled object. For example, the control unit 102 controls the motor 130 that rotates the mounting unit 120. For example, the control unit 102 controls the motor 130 to control the rotation angle of the mounting unit 120. For example, the control unit 102 controls the motor 130 to control the rotation speed of the mounting unit 120. The motor 130 may control the motor 130 to control the rotation direction of the mounting unit 120.

[0048] The control unit 102 controls the LiDAR 110. For example, the control unit 102 controls the output power of the laser light output by the LiDAR 110. The control unit 102 may control the output timing of the laser light output by the LiDAR 110.

[0049] The acquisition unit 104 acquires the measurement results measured by the LiDAR 110. The acquisition unit 104 acquires the measurement results measured by the LiDAR 110 while rotating the mounting unit 120 using the motor 130, for example, to generate a 3D map.

[0050] The measurement result includes, for example, a measured distance between an output position where the output unit 115 outputs the laser light and a point where the laser light is reflected. The measurement result includes, for example, a received light intensity of the reflected light when the laser light is reflected at the point. The measurement result includes, for example, an amount of rotation of the motor 130 when the laser outputs the laser light at the output position.

[0051] The generating unit 106 generates a 3D map. The generating unit 106 generates, for example, a background 3D map. The generating unit 106 generates, for example, a detection 3D map.

[0052] The generating unit 106 generates a 3D map based on, for example, the measurement results acquired by the acquiring unit 104. The generating unit 106 generates the 3D map by, for example, associating an output position where the output unit 115 outputs the laser light with a measured distance between the output position and a point where the laser light is reflected.

[0053] The communication unit 108 communicates with the information processing device 200. The communication unit 108 communicates with the information processing device 200 via the network 20, for example.

[0054] The communication unit 108 transmits, for example, the measurement results acquired by the acquisition unit 104 to the information processing device 200. For example, the communication unit 108 transmits the measurement results acquired by the acquisition unit 104 to the information processing device 200 while the LiDAR 110 is scanning the surroundings. The communication unit 108 may transmit the measurement results acquired by the acquisition unit 104 together to the information processing device 200 in response to completion of scanning the surroundings by the LiDAR 110. The communication unit 108 may transmit the 3D map generated by the generation unit 106 to the information processing device 200.

[0055] 4 illustrates an example of a functional configuration of the information processing device 200. The information processing device 200 includes a storage unit 250, an acquisition unit 252, a generation unit 254, a zone setting unit 256, a detection mode setting unit 257, a division unit 258, a detection unit 260, a monitoring unit 262, a display control unit 264, and a display unit 270. It is not essential that the information processing device 200 includes all of these components.

[0056] The storage unit 250 stores various information. The storage unit 250 stores, for example, detection target information indicating a detection target to be detected using a 3D map. The detection target is, for example, a person. The detection target is, for example, an animal. The detection target is, for example, a car. The detection target is, for example, a bicycle. The detection target is a natural object such as a tree or a stone. The detection target may be any other object. The storage unit 250 stores, for example, monitoring target area information indicating a monitoring target area to be monitored using a 3D map. The monitoring target area is, for example, an event venue. The monitoring target area is, for example, a residential area. The monitoring target area may be any other area. The storage unit 250 may store installation location information indicating the installation location of the sensor 300.

[0057] The acquisition unit 252 acquires various information. The acquisition unit 252 acquires various information by communicating with an external device via the network 20, for example. The acquisition unit 252 may acquire various information by receiving an input from a user of the information processing device 200 via an input unit included in the information processing device 200. The user of the information processing device 200 is, for example, an administrator who manages an area where a 3D map is generated. The user of the information processing device 200 may be any other user. The user of the information processing device 200 may be an example of a user of the system 10. The acquisition unit 252 stores the acquired various information in the storage unit 250.

[0058] The acquisition unit 252 acquires, for example, various pieces of information from the rotary LiDAR 100. The acquisition unit 252 acquires various pieces of information from one rotary LiDAR 100. The acquisition unit 252 may acquire various pieces of information from a plurality of rotary LiDARs 100.

[0059] For example, in order to generate a 3D map, the acquisition unit 252 acquires measurement results measured by the LiDAR 110 while rotating the mounting unit 120 using the motor 130. The acquisition unit 252 may acquire the 3D map.

[0060] The acquisition unit 252 acquires, for example, sensor data from the sensor 300. The acquisition unit 252 acquires, for example, image data from a camera. The acquisition unit 252 may acquire measured distance data from a distance measurement sensor. The measured distance indicated by the measured distance data is the distance between the output position of the beam output from the distance measurement sensor and the point at which the beam is reflected.

[0061] The generating unit 254 generates a 3D map. The generating unit 254 generates, for example, a background 3D map. The generating unit 254 generates, for example, a detection 3D map. The generating unit 254 may store the generated 3D map in the storage unit 250.

[0062] The generating unit 254 generates a 3D map based on, for example, the measurement results stored in the storage unit 250. The generating unit 254 generates the 3D map by associating an output position where the output unit 115 outputs the laser light with a measured distance between the output position and a point where the laser light is reflected.

[0063] When the measurement result includes the received light intensity of the reflected light when the laser light output from the output position is reflected at the point, the generating unit 254 may color-code the pixel corresponding to the point according to the received light intensity. This allows the information processing device 200 to generate a 3D map that allows the user to easily grasp the received light intensity at each point.

[0064] The generation unit 254 generates a 3D map based on, for example, measurement results measured by a plurality of rotary LiDARs 100. In this case, one 3D map is generated by combining the measurement results measured by each rotary LiDAR 100. This allows the information processing device 200 to generate a wider and higher density 3D map.

[0065] The generating unit 254 may generate the 3D map further based on the sensor data stored in the storage unit 250. The generating unit 254 generates the 3D map further based on, for example, image data. The generating unit 254 combines, for example, the image data and the 3D map. This allows the information processing device 200 to generate a 3D map that is more in line with the real world.

[0066] The generating unit 254 may generate a 3D map further based on the measured distance data. For example, the generating unit 254 uses the measured distance data to compensate for the measured distance of a point whose measured distance is not measured by the rotating LiDAR 100. This allows the information processing device 200 to generate a 3D map with even higher density.

[0067] The zone setting unit 256 sets zones for the 3D map stored in the storage unit 250. For example, the zone setting unit 256 sets a plurality of zones for the 3D map. The zone setting unit 256 may store in the storage unit 250 zone information indicating the zones set for the 3D map.

[0068] For example, the zone setting unit 256 sets three-dimensional zones for the 3D map. The zone setting unit 256 may set two-dimensional zones for the 3D map.

[0069] The zone setting unit 256 sets zones for the 3D map generated by the generation unit 254, for example. The zone setting unit 256 sets zones for the 3D map generated by the generation unit 106, for example.

[0070] The zone setting unit 256 sets zones for, for example, the background 3D map. The zone setting unit 256 sets zones for, for example, the detection 3D map.

[0071] The zone setting unit 256 sets a zone on the 3D map, for example, by being specified by the user of the information processing device 200. The user of the information processing device 200 sets a zone on the 3D map, for example, by performing a click operation using an input unit provided in the information processing device 200. This allows the information processing device 200 to set a zone on the 3D map with a simple click operation, thereby reducing the burden on the user of the information processing device 200 when setting a zone on the 3D map.

[0072] The zone setting unit 256 sets zones of any shape for the 3D map, for example. When setting a three-dimensional zone for the 3D map, for example, the zone setting unit 256 sets zones of shapes such as a triangular pyramid, a quadrangular pyramid, other polygonal pyramids, a triangular prism, a quadrangular prism, other polygonal prisms, a cone, a cylinder, an ellipsoid, a sphere, etc. When setting a two-dimensional zone for the 3D map, for example, the zone setting unit 256 sets zones of shapes such as a triangle, a quadrangle, other polygons, an ellipse, a circle, etc.

[0073] The zone setting unit 256 sets, for example, a zone having a shape desired by the user of the information processing device 200 on a 3D map. The zone setting unit 256 sets, for example, a zone having a shape of an area in which the user of the information processing device 200 wants to detect a detection target. The zone setting unit 256 sets, for example, a zone having a shape of an area in which the user of the information processing device 200 wants to monitor.

[0074] In conventional systems, only zones with simple shapes such as circles and squares could be set on a 3D map. In contrast, the information processing device 200 can set zones with more complex shapes. This allows the information processing device 200 to set zones on a 3D map in shapes that are more in line with the preferences of the user of the information processing device 200. In addition, since the information processing device 200 sets zones on a high-density 3D map, it can set zones with an accuracy of about 3 cm, which cannot be achieved with a surveillance camera.

[0075] When multiple zones are set for the 3D map, the zone setting unit 256 may set a color for each zone so that the zones are color-coded. This allows the information processing device 200 to set multiple zones so that the user of the information processing device 200 can more easily understand each zone.

[0076] The detection mode setting unit 257 sets a detection mode for detecting a detection target that may be included in the 3D map. The detection mode setting unit 257 may set a detection mode for each zone set for the 3D map, for example. The detection mode setting unit 257 sets the detection mode by being designated by the user of the information processing device 200, for example.

[0077] The detection mode includes, for example, a normal mode. The detection mode includes, for example, a crowded mode. The specific process in which the information processing device 200 detects the detection target in the normal mode and the specific process in which the information processing device 200 detects the detection target in the crowded mode will be described in detail later.

[0078] The dividing unit 258 divides the detection 3D map stored in the storage unit 250. For example, the dividing unit 258 divides a horizontal plane in the map space of the detection 3D map into a plurality of grid cells whose grid cell size is determined based on the size of the detection target indicated by the detection target information stored in the storage unit 250. For example, the dividing unit 258 divides the horizontal plane in the map space of the detection 3D map into a plurality of grid cells whose grid cell size is n×n.

[0079] The detection section 260 detects a predetermined detection target. For example, the detection section 260 detects the detection target indicated by the detection target information stored in the storage section 250. The detection section 260 may store the detection result in the storage section 250.

[0080] The detection unit 260 detects, for example, a detection target that exists in an area corresponding to a detection 3D map stored in the storage unit 250. The detection unit 260 detects a detection target that exists in an area of ​​the detection 3D map that corresponds to a zone indicated by the zone information stored in the storage unit 250.

[0081] The detection unit 260 detects the detection target based on, for example, the 3D map generated by the generation unit 254. The detection unit 260 detects the detection target based on, for example, the 3D map generated by the generation unit .

[0082] Here, a common portion between the process in which detection unit 260 detects a detection target in normal mode and the process in which detection unit 260 detects a detection target in dense mode will be described.

[0083] The detection unit 260 detects the detection target based on, for example, the background 3D map and the detection 3D map. For example, in order to detect the detection target, the detection unit 260 aligns the detection 3D map with the background 3D map to locate a plurality of points P d and multiple points P of the background 3D map b and each of the above.

[0084] The detection unit 260 aligns the detection 3D map and the background 3D map, for example, by executing an ICP (Iterative Closest Point) algorithm. For example, the detection unit 260 roughly aligns the detection 3D map and the background 3D map by executing a RANSAC (Random Sample Consensus) algorithm before executing the ICP algorithm. Then, the detection unit 260 precisely aligns the detection 3D map and the background 3D map by executing the ICP algorithm. The detection unit 260 may align the detection 3D map and the background 3D map by executing the ICP algorithm without executing the RANSAC algorithm.

[0085] The detection unit 260 executes the ICP algorithm, for example, at a predetermined time interval. The detection unit 260 executes the ICP algorithm, for example, at a time interval of 5 to 10 minutes. This allows the information processing device 200 to correct the misalignment between the detection 3D map and the background 3D map caused by fluctuations in the rotation speed of the motor 130 mounted on the rotary LiDAR 100.

[0086] Detect multiple points P of the 3D map d and multiple points P of the background 3D map b After the points P d Point P of d From the point P d The point P associated with b If the detection unit 260 determines that the distance d in the map space to the point P is longer than the distance threshold δ, d The target point P t On the other hand, when the detection unit 260 determines that d is shorter than δ, it detects the point P d Delete.

[0087] The information processing device 200 detects a point P dBy filtering out the detection 3D map as being indistinguishable from the background 3D map, the points P t Then, the information processing device 200 extracts only the extracted point P t This allows the information processing device 200 to detect the detection target at high speed without using a GPU, thereby enabling the information processing device 200 to detect the detection target at high speed and at low cost.

[0088] Next, a process specific to the case where the detection unit 260 detects the detection target in the normal mode will be described. t The detection unit 260 performs clustering on the points P that are clustered as belonging to one cluster as a result of the clustering, and detects the detection target based on the clustering result. t The number of targets is a predetermined target score threshold N t On the other hand, the detection unit 260 determines that the detection target exists in the area corresponding to the one cluster when the number of points P t The number of t If the number of clusters is smaller than N, it is determined that the detection target does not exist in the area corresponding to the cluster. In this case, the detection unit 260 may regard the cluster as noise and delete the cluster. t may be set according to the detection target.

[0089] The detection unit 260 may determine whether or not a detection target exists in the area corresponding to the cluster, further based on the shape and size of the cluster. t The number of t In the case where the similarity between the shape and size of the cluster and the shape and size of the detection target is higher than a predetermined similarity threshold value in the cluster, the detection unit 260 determines that the detection target exists in the area corresponding to the cluster. t The number of tIf the similarity between the shape and size of a larger cluster and the shape and size of the detection target is lower than a similarity threshold, it is determined that the detection target is not present in the area corresponding to the larger cluster.

[0090] The detection unit 260 executes, for example, a clustering algorithm to detect a plurality of points P t The detection unit 260 performs clustering on the multiple points P by executing, for example, a density-based spatial clustering of applications with noise (DBSCAN) algorithm. t Cluster the following:

[0091] The detection unit 260 detects, for example, a plurality of points P t By classifying multiple points P t The detection unit 260 performs clustering on the detected points P t For each of the points, one target point P' t Another target point P'' whose distance in the map space is shorter than a predetermined distance threshold ε t The number of the first target points is a predetermined threshold value N c If more than P' t Let P be the core point. c Classify into point P'' t The number of c Less than and core point P c is point P'' t If point P' is included in t Let P be the boundary point e Classify into point P'' t The number of c Less than and core point P c is point P'' t If point P' is not included in t At the noise point P n The detection unit 260 classifies the points P t Without classifying multiple P t may be clustered.

[0092] By detecting the detection target in the normal mode, the information processing device 200 can detect the detection target present in a distant area where the pixel density is too low to detect the detection target using a deep learning-based method. In addition, by detecting the detection target in the normal mode, the information processing device 200 can increase robustness against noise and detect the detection target more accurately.

[0093] Next, a process specific to the case where the detection unit 260 detects a detection target in the dense mode will be described. The detection unit 260 detects a detection target based on, for example, a plurality of grid cells divided by the division unit 258. The detection unit 260 detects a detection target by, for example, executing a grid occupancy algorithm.

[0094] The detection unit 260 detects, for example, a point P t The number of targets is a predetermined target score threshold N g On the other hand, the detection unit 260 determines that the detection target exists in an area corresponding to a larger number of grid cells. t The number of g In this case, the detection unit 260 determines that the detection target does not exist in the area corresponding to the fewer grid cells. t is regarded as noise, and the point P t You may delete N. g may be set according to the detection target.

[0095] A point P in the vertical direction in the map space of a grid cell t For example, a point P that lies above a grid cell in map space t A point P in the vertical direction in the map space of a grid cell t For example, a point P that exists on a grid cell in the map space is t A point P in the vertical direction in the map space of a grid cell tFor example, a point P that lies below a grid cell in map space t Includes.

[0096] By detecting the detection target in the dense mode, the information processing device 200 can efficiently and accurately detect the detection target that exists in a distant area where the detection targets are densely packed and the number of pixels per detection target is small.

[0097] When the detection unit 260 detects that a plurality of detection targets exists in an area corresponding to the detection 3D map in the normal mode or the dense mode, the detection unit 260 may count the number of detection targets existing in the area corresponding to the detection 3D map. When the detection unit 260 detects that a plurality of detection targets exists in an area corresponding to a zone in the normal mode or the dense mode, the detection unit 260 may count the number of detection targets existing in the area corresponding to the zone.

[0098] The monitoring unit 262 monitors the monitoring target area. For example, the monitoring unit 262 monitors the monitoring target area indicated by the monitoring target area information stored in the storage unit 250. The monitoring unit 262 may store the monitoring result in the storage unit 250.

[0099] The monitored area is, for example, an area corresponding to the detected 3D map in the storage unit 250. The monitored area is an area corresponding to a zone indicated by the zone information stored in the storage unit 250 in the detected 3D map.

[0100] The monitoring unit 262 monitors the monitored area based on, for example, the detection result by the detection unit 260. The monitoring unit 262 monitors, for example, whether or not the position of the detection target detected by the detection unit 260 is included in the monitored area. This allows the information processing device 200 to monitor whether or not the detection target has entered the monitored area using a 3D map when the monitored area is a no-entry area, a dangerous area, or the like.

[0101] The monitoring unit 262 monitors, for example, the number of detection targets present in the monitoring target area. For example, when the number of detection targets present in the monitoring target area is greater than a predetermined number, the monitoring unit 262 determines that the monitoring target area is in a congested state. This allows the information processing device 200 to monitor the congestion state of the monitoring target area using a 3D map.

[0102] The display control unit 264 controls the display of the display unit 270. The display control unit 264 controls the display of the display unit 270 based on, for example, various information stored in the storage unit 250. The display unit 270 may display various information in accordance with the control by the display control unit 264.

[0103] The display control unit 264 controls the display of the display unit 270, for example, to display a background 3D map. The display control unit 264 controls the display of the display unit 270, for example, to display a background 3D map with a zone indicated by the zone information superimposed thereon. For example, when a plurality of zones are superimposed on the background 3D map, the display control unit 264 controls the display of the display unit 270, for example, to display a background 3D map with each zone being color-coded. The display control unit 264 controls the display of the display unit 270, for example, to display a background 3D map with each pixel being color-coded according to the received light intensity included in the measurement result.

[0104] The display control unit 264 displays the display of the display unit 270 so as to display, for example, a detection 3D map. The display control unit 264 controls the display of the display unit 270 so as to display, for example, a detection 3D map in a state in which a zone indicated by the zone information is superimposed. For example, when a plurality of zones are superimposed on the detection 3D map, the display control unit 264 controls the display of the display unit 270 so as to display the detection 3D map in a state in which the zones are color-coded for each zone. For example, the display control unit 264 controls the display of the display unit 270 so as to display the detection 3D map in a state in which each pixel is color-coded according to the received light intensity included in the measurement result. For example, the display unit 270 controls the display of the display unit 270 so as to display the detection 3D map in a state in which the detection 3D map is divided into a plurality of grid cells.

[0105] The display control unit 264 controls the display of the display unit 270, for example, to display the detection 3D map with a detection result by the detection unit 260 superimposed thereon. The display control unit 264 controls the display of the display unit 270, for example, to display the detection 3D map with a bounding box encompassing an occupied area of ​​the detection target in the detection 3D map superimposed thereon.

[0106] 5 is an explanatory diagram for explaining an example in which the information processing device 200 aligns the detection 3D map and the background 3D map. Here, an example in which the information processing device 200 aligns the detection 3D map and the background 3D map by executing the ICP algorithm will be mainly described. Note that the "◯" in FIG. 5 indicates a point P b and "▽" is the point P of the detected 3D map. d It is assumed that.

[0107] FIG. 5A shows a case where the information processing device 200 detects a plurality of points P d Each of these is treated as multiple points P bFIG. 1 is an explanatory diagram for explaining an example of a process (sometimes referred to as "process 1") for matching the detected 3D map with any of the above. Here, the explanation will be continued on the assumption that the detection unit 260 executes the RANSAC algorithm to align the detected 3D map with the background 3D map in advance.

[0108] The detection unit 260 detects, for example, the point P d The point P with the shortest distance in the map space from b By identifying the point P d Point P b The detection unit 260 may, for example, perform a nearest neighbor search to find the point P d The point P with the shortest distance in the map space from b For example, when performing the nearest neighbor search, the detection unit 260 detects a plurality of points P b In the example shown in FIG. 5A, the detection unit 260 detects a point P d1 Point P b1 and match the point P d2 Point P b3 and match the point P d3 Point P b3 and match the point P d4 Point P b4 and match the point P d5 Point P b4 and point P d6 Point P b6 The explanation will continue assuming that it matches.

[0109] 5B is an explanatory diagram for explaining an example of a process (sometimes referred to as "process 2") in which the information processing device 200 determines an alignment matrix for aligning the detection 3D map with the background 3D map. Here, an example in which the detection unit 260 determines the alignment matrix from the matching result in process 1 shown in FIG. 5A will be mainly described.

[0110] The detection unit 260 detects a plurality of points P dFor each of the points P d The point P d The matched point P b 5(A) , the detection unit 260 determines a position error in the map space for the point P , and determines a registration matrix such that the sum of the position errors is minimized. The registration matrix includes, for example, a rotation matrix. The registration matrix includes, for example, a translation matrix. In the example shown in FIG. 5(B) , the detection unit 260 determines a position error in the map space for the point P , and determines a registration matrix such that the sum of the position errors is minimized. The registration matrix includes, for example, a rotation matrix. The registration matrix includes, for example, a translation matrix. d1 Point P b1 The position error e1 in the map space for point P d2 Point P b3 The position error e2 in the map space for point P d3 Point P b3 The error e3 in the map space for point P d4 Point P b4 The error e4 in the map space for point P d5 Point P b4 The error e5 in the map space for the position of point P d6 Point P b6 The sum of the position errors e6 in the map space for total Determine the alignment matrix so that e1+e2+e3+e4+e5+e6 is minimized.

[0111] 5C is an explanatory diagram for explaining an example of a process (sometimes referred to as "process 3") in which the information processing device 200 determines whether or not the positional relationship between the detected 3D map and the background 3D map satisfies a predetermined end condition. Here, the explanation will be continued assuming that the detection unit 260 aligns the detected 3D map with the background 3D map using the alignment matrix determined by process 2 shown in FIG. 5B.

[0112] When the detection unit 260 determines that the positional relationship between the detected 3D map and the background 3D map satisfies the end condition, the detection unit 260 may end the process of aligning the detected 3D map and the background 3D map. dand multiple points P in the background 3D map. b and complete the process of associating each of the detected 3D map and the background 3D map. On the other hand, if the detection unit 260 determines that the positional relationship between the detected 3D map and the background 3D map does not satisfy the end condition, the detection unit 260 may execute process 1 again. In this case, the detection unit 260 repeatedly executes processes 1 to 3 until it determines that the positional relationship between the detected 3D map and the background 3D map satisfies the end condition.

[0113] The termination condition is, for example, a point P of the detection 3D map after the detection 3D map is aligned with the background 3D map. d and the point P in the background 3D map b The sum of the position errors in the map space of each of the points P and P2 is smaller than a predetermined error threshold. The detection unit 260 may, for example, perform a nearest neighbor search to find the point P of the detected 3D map after the detected 3D map is aligned with the background 3D map. d The point P with the shortest distance in the map space from b By identifying the point P d Point P b In the example shown in FIG. 5C, the detection unit 260 matches the point P d1 Point P b1 and match the point P d2 Point P b2 and match the point P d3 Point P b3 and match the point P d4 Point P b4 and match the point P d5 Point P b5 and point P d6 Point P b6 Then, the detection unit 260 may match the point P d1 Point P b1 The position error e'1 in the map space for point P d2 Point P b2 The position error e'2 in the map space for point P d3 Point P b3 The position error e'3 in the map space for point P d4 Point Pb4 The position error e'4 in the map space for point P d5 Point P b5 The position error e'5 in the map space for point P d6 Point P b6 The sum of the position errors e'6 in the map space for total If =e'1+e'2+e'3+e'4+e'5+e'6 is smaller than the error threshold, it may be determined that the positional relationship between the detection 3D map and the background 3D map satisfies the termination condition.

[0114] 6 is an explanatory diagram for explaining an example in which the information processing device 200 detects a detection target in normal mode. Here, the information processing device 200 executes the DBSCAN algorithm to detect a plurality of points P t Here, an example of detecting a detection target from the above will be mainly described. Note that the "□" in Fig. 6 is assumed to be a target point.

[0115] 6A is an explanatory diagram for explaining an example in which the detection unit 260 detects another target point whose distance to a certain target point in the map space is shorter than ε. Here, the detection unit 260 detects a point P t1 Let us detect other target points whose distance to the point P in the map space is shorter than ε. t1 is a plurality of points P t The explanation will continue assuming that this is the starting point of the classification process.

[0116] In the example shown in FIG. 6A, the detection unit 260 detects a point P t1 As another target point whose distance to the map space is shorter than ε, point P t2 , point P t3 and point P t4 After that, the detection unit 260 detects the point P t1 The number of other target points corresponding to c In the example shown in FIG. c The explanation will continue assuming that =2.

[0117] In the example shown in FIG. 6A, point P t1 The number of other target points corresponding to point P t2 , point P t3 and point P t4 A total of 3>N c Therefore, the detection unit 260 detects the point P t1 Let P be the core point. c1 Then, the detection unit 260 classifies the core points P c1 It is determined that belongs to cluster 1.

[0118] 6B is an explanatory diagram for explaining another example in which the detection unit 260 detects another target point whose distance to the one target point in the map space is shorter than ε. Here, the detection unit 260 detects a point P t2 Other target points whose distance in the map space to point P is shorter than ε, t4 It is assumed that the distance to the target point t1 in the map space is shorter than ε.

[0119] In the example shown in FIG. 6B, the detection unit 260 detects a point P t2 As another target point whose distance to the map space is shorter than ε, point P t1 , point P t3 and point P t5 After that, the detection unit 260 detects the point P t2 The number of other target points corresponding to c In the example shown in FIG. 6B, the point P t2 The number of other target points corresponding to point P t1 , point P t3 and point P t5 A total of 3>N c Therefore, the detection unit 260 detects the point P t2 Let P be the core point. c2 Then, the detection unit 260 classifies the core points P c2 It is determined that belongs to cluster 1.

[0120] In the example shown in FIG. 6B, the detection unit 260 detects a point P t4As another target point whose distance to the map space is shorter than ε, point P t1 and point P t6 After that, the detection unit 260 detects the point P t4 The number of other target points corresponding to c In the example shown in FIG. 6B, the point P t4 The number of other target points corresponding to point P t1 and point P t6 Total of 2 = N c And the core point P c1 is point P t4 Since the point P is included in the other target points corresponding to the point P, the detection unit 260 t4 Let P be the boundary point e4 Then, the detection unit 260 classifies the boundary points P e4 It is determined that belongs to cluster 1.

[0121] 6C is an explanatory diagram for explaining another example in which the detection unit 260 detects another target point whose distance to the one target point in the map space is shorter than ε. Here, the detection unit 260 detects a point P t6 Other target points whose distance in the map space to point P is shorter than ε, t7 It is assumed that the distance to the target point t1 in the map space is shorter than ε.

[0122] In the example shown in FIG. 6C, the detection unit 260 detects a point P t6 As another target point whose distance to the map space is shorter than ε, point P t4 After that, the detection unit 260 detects the point P t6 The number of other target points corresponding to c In the example shown in FIG. 6C, the point P t6 The number of other target points corresponding to point P t4 A total of 1 <N c And the core point is point P t6 Since the point P is not included in the other target points corresponding to the point P t6 At the noise point P n6Then, the detection unit 260 classifies the noise points P n6 Remove from the detection 3D map.

[0123] In the example shown in FIG. 6C, the detection unit 260 detects a point P t7 Since there is no other target point whose distance in the map space to is shorter than ε, point P t7 At the noise point P n7 Then, the detection unit 260 classifies the noise points P n7 is removed from the detection 3D map. If the number of other target points whose distance in the map space to a target point is shorter than ε is 0, the number of such other target points is c This may be an example of a case where the core point is less and is not included in other target points.

[0124] The detection unit 260 detects the point P t The classification process may be performed until there are no more points P t 6, the detection unit 260 detects the detection target based on the clustering result of the points P t The number of t If the number of clusters is greater than the number of clusters, it is determined that the detection target is present in the area corresponding to cluster 1.

[0125] The number of other target points whose distance to a target point in the map space is shorter than ε is N. c If so, the detection unit 260 detects the one target point as a core point P c The process of not classifying the target point as a target point is performed by dividing the number of other target points whose distance to the target point in the map space is shorter than ε by the number of other target points = N c If so, the detection unit 260 detects the one target point as a core point P c This may be replaced by a process of classifying the

[0126] 7 is an explanatory diagram for explaining an example in which the information processing device 200 detects a detection target in a dense mode. Here, the information processing device 200 executes a grid occupancy algorithm to detect a plurality of points P t The following mainly describes an example of detecting the detection target from the point P. t It is assumed that.

[0127] 7, the map space is represented by an XYZ coordinate system. Here, the description will be continued assuming that the dividing unit 258 divides the XY plane of Z=0 in the map space of the detection 3D map into a plurality of grid cells whose grid cell size is determined based on the size of the detection target.

[0128] The detection unit 260 detects the detection target based on the plurality of grid cells divided by the division unit 258. For example, the detection unit 260 detects a point P t The number of g It is determined that the detection target exists in an area corresponding to a larger number of grid cells.

[0129] In the example shown in FIG. 7, the detection unit 260 detects a point P t The number of g 7, the detection unit 260 determines that the detection target exists in the area corresponding to grid cell 1. On the other hand, in the example shown in FIG. t The number of g Since the number of pixels is smaller than the number of pixels in the grid cell 2, it is determined that the detection target does not exist in the area corresponding to the grid cell 2. In this case, the detection unit 260 detects the point P t is regarded as noise, and the point P t may be deleted.

[0130] 8 is an explanatory diagram for explaining an example of a processing flow of the system 10. Here, an example of a processing flow when the information processing device 200 executes initial settings for detecting a detection target will be mainly described.

[0131] In step (sometimes abbreviated to S) 102, the acquisition unit 252 acquires measurement results measured by the rotating LiDAR 100 from the rotating LiDAR 100 via the network 20. The generation unit 254 generates a background 3D map based on the measurement results acquired by the acquisition unit 252.

[0132] In S104, the zone setting unit 256 sets a plurality of zones for the background 3D map generated in S102 by the generation unit 254. In S106, the generation unit 254 stores the background 3D map generated in S102 in the storage unit 250, and the zone setting unit 256 stores in the storage unit 250 zone information indicating the plurality of zones set in S104.

[0133] In S108, the display control unit 264 controls the display of the display unit 270 so as to display the background 3D map generated by the generation unit 254 in S102 in a state in which the multiple zones set by the zone setting unit 256 in S104 are superimposed. The display unit 270 displays the background 3D map in a state in which the multiple zones are superimposed, in accordance with the control by the display control unit 264.

[0134] 9 is an explanatory diagram for explaining another example of the processing flow of the system 10. Here, an example of the processing flow when the information processing device 200 detects a detection target in normal mode will be mainly described.

[0135] In S202, the acquisition unit 252 acquires a measurement result measured by the rotating LiDAR 100 from the rotating LiDAR 100 via the network 20. The generation unit 254 generates a detection 3D map based on the measurement result acquired by the acquisition unit 252. The detection unit 260 filters the detection 3D map by comparing the background 3D map stored in the storage unit 250 with the detection 3D map generated by the generation unit 254.

[0136] In S204, the detection unit 260 executes a clustering algorithm on the detection 3D map filtered in S202 to detect detection targets present in the areas corresponding to each zone. In S206, the detection unit 260 stores the detection result including the number of detection targets present in the areas corresponding to each zone in the storage unit 250.

[0137] In S208, the display control unit 264 controls the display of the display unit 270 so as to display the detection 3D map generated by the generation unit 254 in S202 in a state in which a bounding box encompassing the occupation area of ​​the detection target detected by the detection unit 260 in S204 is superimposed. The display unit 270 displays the detection 3D map in a state in which the bounding box is superimposed, in accordance with the control of the display control unit 264.

[0138] 10 is an explanatory diagram for explaining another example of the processing flow of the system 10. Here, an example of the processing flow when the information processing device 200 detects a detection target in the crowded mode will be mainly described.

[0139] In S302, the acquisition unit 252 acquires a measurement result measured by the rotating LiDAR 100 from the rotating LiDAR 100 via the network 20. The generation unit 254 generates a detection 3D map based on the measurement result acquired by the acquisition unit 252. The detection unit 260 filters the detection 3D map by comparing the background 3D map stored in the storage unit 250 with the detection 3D map generated by the generation unit 254.

[0140] In S304, the detection unit 260 executes a grid occupancy algorithm on the detection 3D map filtered in S202 to detect detection targets present in areas corresponding to each zone. In S306, the detection unit 260 stores the detection result including the number of detection targets present in the areas corresponding to each zone in the storage unit 250.

[0141] In S308, the display control unit 264 controls the display of the display unit 270 so as to display the detection 3D map generated by the generation unit 254 in S302 in a state in which a bounding box encompassing the occupation area of ​​the detection target detected by the detection unit 260 in S304 is superimposed. The display unit 270 displays the detection 3D map in a state in which the bounding box is superimposed, in accordance with the control of the display control unit 264.

[0142] 11 is a schematic diagram showing an example of a hardware configuration of a computer 1200 functioning as the rotating LiDAR 100 or the information processing device 200. A program installed in the computer 1200 can cause the computer 1200 to function as one or more "parts" of the device according to the above embodiment, or cause the computer 1200 to execute operations or one or more "parts" associated with the device according to the above embodiment, and / or cause the computer 1200 to execute a process or steps of the process according to the above embodiment. Such a program may be executed by the CPU 1212 to cause the computer 1200 to execute specific operations associated with some or all of the blocks in the flowcharts and block diagrams described herein.

[0143] The computer 1200 according to this embodiment includes a CPU 1212, a RAM 1214, and a graphics controller 1216, which are connected to each other by a host controller 1210. The computer 1200 also includes input / output units such as a communication interface 1222, a storage device 1224, a DVD drive 1226, and an IC card drive, which are connected to the host controller 1210 via an input / output controller 1220. The DVD drive 1226 may be a DVD-ROM drive, a DVD-RAM drive, or the like. The storage device 1224 may be a hard disk drive, a solid state drive, or the like. The computer 1200 also includes legacy input / output units such as a ROM 1230 and a keyboard 1242, which are connected to the input / output controller 1220 via an input / output chip 1240.

[0144] The CPU 1212 operates according to a program stored in the ROM 1230 and the RAM 1214, thereby controlling each unit. The graphic controller 1216 acquires image data generated by the CPU 1212 into a frame buffer or the like provided in the RAM 1214 or into itself, and causes the image data to be displayed on the display device 1218.

[0145] The communication interface 1222 communicates with other electronic devices via a network. The storage device 1224 stores programs and data used by the CPU 1212 in the computer 1200. The DVD drive 1226 reads programs or data from a DVD-ROM 1227 or the like and provides the programs or data to the storage device 1224. The IC card drive reads programs and data from an IC card and / or writes programs and data to an IC card.

[0146] The ROM 1230 stores therein a boot program or the like that is executed by the computer 1200 upon activation, and / or a program that depends on the hardware of the computer 1200. The input / output chip 1240 may also connect various input / output units to the input / output controller 1220 via a USB port, a parallel port, a serial port, a keyboard port, a mouse port, and the like.

[0147] The programs are provided by a computer-readable storage medium such as a DVD-ROM 1227 or an IC card. The programs are read from the computer-readable storage medium, installed in the storage device 1224, the RAM 1214, or the ROM 1230, which are also examples of computer-readable storage media, and executed by the CPU 1212. Information processing described in these programs is read by the computer 1200, and brings about cooperation between the programs and the various types of hardware resources described above. An apparatus or method may be constructed by implementing operations or processing of information according to the use of the computer 1200.

[0148] For example, when communication is performed between the computer 1200 and an external device, the CPU 1212 may execute a communication program loaded in the RAM 1214 and instruct the communication interface 1222 to perform communication processing based on the processing described in the communication program. Under the control of the CPU 1212, the communication interface 1222 reads transmission data stored in a transmission buffer area provided in the RAM 1214, the storage device 1224, the DVD-ROM 1227, or a recording medium such as an IC card, and transmits the read transmission data to a network, or writes reception data received from the network to a reception buffer area or the like provided on the recording medium.

[0149] Furthermore, the CPU 1212 may cause all or a necessary portion of a file or database stored in an external recording medium such as the storage device 1224, the DVD drive 1226 (DVD-ROM 1227), an IC card, etc. to be read into the RAM 1214, and perform various types of processing on the data on the RAM 1214. The CPU 1212 may then write back the processed data to the external recording medium.

[0150] Various types of information, such as various types of programs, data, tables, and databases, may be stored in the recording medium and undergo information processing. The CPU 1212 may perform various types of processing on the data read from the RAM 1214, including various types of operations, information processing, conditional judgment, conditional branching, unconditional branching, information search / replacement, etc., as described throughout this disclosure and specified by the instruction sequence of the program, and write back the results to the RAM 1214. The CPU 1212 may also search for information in a file, database, etc. in the recording medium. For example, when a plurality of entries each having an attribute value of a first attribute associated with an attribute value of a second attribute are stored in the recording medium, the CPU 1212 may search for an entry whose attribute value of the first attribute matches a specified condition from among the plurality of entries, read the attribute value of the second attribute stored in the entry, and thereby obtain the attribute value of the second attribute associated with the first attribute that satisfies a predetermined condition.

[0151] The above-described programs or software modules may be stored in a computer-readable storage medium on the computer 1200 or in the vicinity of the computer 1200. In addition, a recording medium such as a hard disk or a RAM provided in a server system connected to a dedicated communication network or the Internet can be used as a computer-readable storage medium, thereby providing the programs to the computer 1200 via the network.

[0152] The blocks in the flowcharts and block diagrams in the present embodiment may represent stages of a process in which an operation is performed or "parts" of an apparatus responsible for performing the operation. Particular stages and "parts" may be implemented by dedicated circuitry, programmable circuitry provided with computer-readable instructions stored on a computer-readable storage medium, and / or a processor provided with computer-readable instructions stored on a computer-readable storage medium. The dedicated circuitry may include digital and / or analog hardware circuits, and may include integrated circuits (ICs) and / or discrete circuits. The programmable circuitry may include reconfigurable hardware circuits, such as, for example, field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), and the like, including AND, OR, XOR, NAND, NOR, and other logical operations, flip-flops, registers, and memory elements.

[0153] A computer-readable storage medium may include any tangible device capable of storing instructions that are executed by a suitable device, such that a computer-readable storage medium having instructions stored thereon comprises an article of manufacture that includes instructions that can be executed to create means for performing the operations specified in the flowcharts or block diagrams. Examples of computer-readable storage media may include electronic storage media, magnetic storage media, optical storage media, electromagnetic storage media, semiconductor storage media, and the like. More specific examples of computer-readable storage media may include floppy disks, diskettes, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), electrically erasable programmable read-only memories (EEPROMs), static random access memories (SRAMs), compact disk read-only memories (CD-ROMs), digital versatile disks (DVDs), Blu-ray disks, memory sticks, integrated circuit cards, and the like.

[0154] The computer readable instructions may include either assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk®, JAVA®, C++, etc., and conventional procedural programming languages ​​such as the “C” programming language or similar programming languages.

[0155] Computer readable instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, or to a programmable circuit, either locally or over a local area network (LAN), a wide area network (WAN), such as the Internet, etc., to cause the processor of the general purpose computer, special purpose computer, or other programmable data processing apparatus, or to a programmable circuit, to execute the computer readable instructions to generate means for performing the operations specified in the flowcharts or block diagrams. Examples of processors include computer processors, processing units, microprocessors, digital signal processors, controllers, microcontrollers, etc.

[0156] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear to those skilled in the art that various modifications and improvements can be made to the above embodiments. It is clear from the description of the claims that such modifications and improvements can also be included in the technical scope of the present invention.

[0157] It should be noted that the order of execution of each process, such as operations, procedures, steps, and stages, in the devices, systems, programs, and methods shown in the claims, specifications, and drawings is not specifically stated as "before" or "prior to," and that the process may be performed in any order unless the output of a previous process is used in a later process. Even if the operational flow in the claims, specifications, and drawings is described using "first," "next," etc. for convenience, it does not mean that the process must be performed in this order. [Explanation of symbols]

[0158] 10 system, 20 network, 100 rotating LiDAR, 102 control unit, 104 acquisition unit, 106 generation unit, 108 communication unit, 110 LiDAR, 115 output unit, 120 mounting unit, 130 motor, 150 housing, 200 information processing device, 250 memory unit, 252 acquisition unit, 254 generation unit, 256 zone setting unit, 257 detection mode setting unit, 258 division unit, 260 detection unit, 262 monitoring unit, 264 display control unit, 270 display unit, 300 sensor, 1200 computer, 1210 host controller, 1212 CPU, 1214 RAM, 1216 graphics controller, 1218 display device, 1220 input / output controller, 1222 communication interface, 1224 storage device, 1226 DVD drive, 1227 DVD-ROM, 1230 ROM, 1240 I / O chip, 1242 keyboard

Claims

1. A mounting unit that mounts a LiDAR (Light Detection and Ranging) device; A motor that rotates the mounting portion; an acquisition unit that acquires measurement results measured by the LiDAR while rotating the mounting unit using the motor in order to generate a 3D map; A system comprising:

2. The system of claim 1 , further comprising a generator configured to generate the 3D map based on the measurement results.

3. The system according to claim 2 , further comprising a detection unit that detects a predetermined detection target based on the 3D map generated by the generation unit.

4. The system of claim 3, wherein the detection unit detects the detection target by aligning the 3D map generated by the generation unit with a 3D map in a state in which the detection target is not included, thereby associating each of a plurality of first points in the 3D map generated by the generation unit with each of a plurality of second points in the 3D map in a state in which the detection target is not included, and detecting a target point whose distance in map space from a first point of the plurality of first points to a second point associated with the first point is longer than a predetermined first distance threshold.

5. 5. The system according to claim 4, wherein the detection unit classifies, for each of the detected multiple target points, a single target point as a core point if a number of other target points whose distances to the single target point in the map space are shorter than a predetermined second distance threshold is greater than a predetermined first target point number threshold, classifies the single target point as a boundary point if the number of the other target points is less than the first target point number threshold and the core point is included in the other target points, and classifies the single target point as a noise point if the number of the other target points is less than the first target point number threshold and the core point is not included in the other target points, clusters the detected multiple target points, and determines that the detection target is present in an area corresponding to the single cluster if a number of target points clustered to belong to the single cluster as a result of the clustering is greater than a predetermined second target point number threshold.

6. The system according to claim 5 , wherein the detection unit determines whether or not the detection target is present in the area corresponding to the one cluster based further on a shape and a size of the one cluster.

7. a division unit that divides a horizontal plane in the map space of the 3D map generated by the generation unit into a plurality of grid cells, the grid cell size of which is determined based on the size of the detection target; Further equipped with the detection unit determines that the detection target is present in an area corresponding to a grid cell among the plurality of grid cells in which the number of the target points existing in a vertical direction in the map space is greater than a predetermined third target point number threshold; The system of claim 4.

8. The system according to claim 2 , further comprising a zone setting unit that sets a zone specified by a user of the system for the 3D map generated by the generation unit.

9. a detection unit that detects, based on the 3D map generated by the generation unit, that a predetermined detection target is present in an area corresponding to the zone set by the zone setting unit; a monitoring unit that monitors the area corresponding to the zone set by the zone setting unit based on a detection result by the detection unit; The system of claim 8 further comprising:

10. The system of claim 1 , further comprising the LiDAR.

11. 1. A computer-implemented method comprising: An acquisition step of acquiring measurement results measured by the LiDAR while rotating the mounting unit on which the LiDAR is mounted using a motor that rotates the mounting unit to generate a 3D map. A method comprising:

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