Information processing device, control method, program and storage medium

Abstract

To provide an information processing device capable of preferably estimating a reflection characteristic.SOLUTION: A controller 43 of a reflection characteristic estimation device 4 acquires point group data D1 measured at a plurality of irradiation positions by a lidar 2 for performing measurement by receiving reflection light of irradiated light. Then, the controller 43 generates integrated point group data D3 obtained by associating the irradiation positions with information on reflection intensity at each measurement point. Then, the controller 43 estimates a reflection characteristic representing a relation between an irradiation angle and the reflection intensity in each cluster of measurement points shown by the integrated point group data D3.SELECTED DRAWING: Figure 7

Description

[Technical field]

[0001] The present invention relates to an estimation technique for reflection characteristics. [Background technology]

[0002] Conventionally, there are known techniques for detecting features around roads. For example, Patent Document 1 discloses a technique for performing image segmentation using semantic segmentation based on an image captured by a camera and reflection characteristics included in point cloud data output by a lidar. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-156973 A Summary of the Invention [Problem to be solved by the invention]

[0004] The reflection characteristics of objects around the road vary, and there are retroreflective materials that have a uniform reflection intensity regardless of the irradiation angle, materials that have a low reflection intensity in a specific direction, etc. Therefore, when accurately estimating the reflection characteristics of an object, it is necessary to take the irradiation direction into consideration.

[0005] The present invention has been made in order to solve the above-mentioned problems, and has as its main object to provide an information processing device capable of suitably estimating reflection characteristics. [Means for solving the problem]

[0006] The claimed invention is an acquisition means for acquiring point cloud data measured at a plurality of irradiation positions by a measurement device that performs measurement by receiving reflected light of irradiated light; an integration processing means for generating integrated point cloud data by integrating the point cloud data into a common coordinate system in which information regarding the irradiation position and the reflection intensity is associated with each measurement point; a clustering processing means for performing clustering so that adjacent measurement points are grouped into the same cluster based on distances between the measurement points constituting the integrated point cloud data; a reflection characteristic estimation means for estimating a reflection characteristic representing a relationship between an irradiation angle and a reflection intensity for each cluster of measurement points represented by the integrated point cloud data generated by the clustering; The information processing device has the following features.

[0007] The claimed invention also includes: A computer-implemented control method, comprising: A measuring device measures the reflected light of the irradiated light at multiple irradiation positions. The measured point cloud data is acquired, generating integrated point cloud data by integrating the point cloud data into a common coordinate system in which information regarding the irradiation position and the reflection intensity is associated with each measurement point; clustering the adjacent measurement points into the same cluster based on the distances between the measurement points constituting the integrated point cloud data; estimating a reflection characteristic representing a relationship between an irradiation angle and a reflection intensity for each cluster of measurement points represented by the integrated point cloud data generated by the clustering; It is a control method.

[0008] The claimed invention also includes: A measuring device measures the reflected light of the irradiated light at multiple irradiation positions. The measured point cloud data is acquired, generating integrated point cloud data by integrating the point cloud data into a common coordinate system in which information regarding the irradiation position and the reflection intensity is associated with each measurement point; clustering the adjacent measurement points into the same cluster based on the distances between the measurement points constituting the integrated point cloud data; The program causes a computer to execute a process of estimating reflection characteristics that indicate the relationship between irradiation angle and reflection intensity for each cluster of measurement points represented by the integrated point cloud data generated by the clustering. [Brief description of the drawings]

[0009] [Figure 1] 1 shows a schematic configuration of a reflection characteristic estimation system. [Diagram 2] FIG. 2 is a block diagram showing a functional configuration of the reflection characteristic estimation device. [Diagram 3] FIG. 2 shows a functional block diagram of a controller of the reflection characteristic estimation device. [Figure 4] (A) is a diagram showing each measurement point of the point cloud data measured when the lidar is at position P1, (B) is a diagram showing each measurement point of the point cloud data measured when the lidar is at position P2, and (C) is a diagram showing each measurement point of the integrated point cloud data. [Diagram 5] (A) shows the data structure of each measurement point shown in Fig. 4(A), (B) shows the data structure of each measurement point shown in Fig. 4(B), and (C) shows the data structure of each measurement point shown in Fig. 4(C). [Figure 6] 1A is a diagram showing the relationship between measurement points obtained for an object 63 and the illumination position of the lidar, and FIG. 1B is a diagram showing the relationship between measurement points obtained for an object 64 and the illumination position of the lidar. [Figure 7] 5 is a flowchart illustrating an example of a processing procedure executed by a controller in the first embodiment. [Figure 8] FIG. 11 is a functional block diagram of a controller according to a second embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] According to a preferred embodiment of the present invention, the information processing device includes: an acquisition unit that acquires point cloud data measured at a plurality of irradiation positions by a measurement device that performs measurement by receiving reflected light of irradiated light; an integration processing unit that generates integrated point cloud data by integrating the point cloud data in which information on the irradiation position and reflection intensity is associated with each measurement point; and a reflection characteristic estimation unit that estimates reflection characteristics that represent the relationship between the irradiation angle and reflection intensity for each cluster of measurement points indicated by the integrated point cloud data. With this aspect, the information processing device can suitably estimate the reflection characteristics of an object represented by a cluster of measurement points based on data obtained by integrating the point cloud data measured by the measurement device.

[0011] In one aspect of the information processing device, the information processing device further includes a physical property estimation unit that estimates a physical property corresponding to each of the clusters based on the reflection property estimated by the reflection property estimation unit. With this aspect, the information processing device can preferably estimate the physical property corresponding to each cluster.

[0012] In another aspect of the information processing device, the physical property estimation means estimates a category related to physical properties to which each of the clusters belongs based on the reflection property estimated by the reflection property estimation means and known physical property information indicating the reflection property for each category having different physical properties. With this aspect, the information processing device can suitably estimate a category related to physical properties corresponding to each cluster.

[0013] In another aspect of the information processing device, the information processing device further includes a clustering processing unit that performs clustering to classify adjacent measurement points into the same cluster based on the distance between the measurement points that constitute the integrated point cloud data. With this aspect, the information processing device can preferably form a cluster for each object.

[0014] In another aspect of the information processing device, the clustering processing means performs the clustering based on the distance and the reflection intensity. With this aspect, the information processing device can more accurately generate a cluster for each object by taking the reflection intensity into consideration.

[0015] In another aspect of the information processing device, the information processing device includes a physical property estimation means for estimating physical properties corresponding to each of the clusters based on the reflection properties estimated by the reflection property estimation means, and the clustering processing means divides the clusters based on the estimation result of the physical property estimation means and the reflection intensity. With this aspect, when a plurality of objects with different physical properties are close to each other and the measurement points of these objects are clustered into one cluster, the information processing device can suitably divide the cluster for each object.

[0016] In another aspect of the information processing device, the reflection characteristic estimating means calculates the irradiation angle for each of the measurement points based on the irradiation position and the measurement position indicated by the measurement point, and estimates the reflection characteristic based on the calculated irradiation angle and the reflection intensity. With this aspect, the information processing device can suitably estimate the reflection characteristic taking into account the irradiation angle.

[0017] In another aspect of the information processing device, the integration processing means generates the integrated point cloud data by converting the point cloud data measured at a plurality of irradiation positions into a common coordinate system based on movement information of the measurement device when measuring the point cloud data. With this aspect, the information processing device can preferably generate the integrated point cloud data.

[0018] According to another preferred embodiment of the present invention, there is provided a control method executed by a computer, which acquires point cloud data measured at a plurality of irradiation positions by a measurement device that performs measurements by receiving reflected light of irradiated light, generates integrated point cloud data by integrating the point cloud data in which information on the irradiation position and reflection intensity is associated with each measurement point, and estimates reflection characteristics representing a relationship between the irradiation angle and reflection intensity for each cluster of measurement points indicated by the integrated point cloud data. By executing this control method, the computer can suitably estimate the reflection characteristics of an object represented by the cluster of measurement points.

[0019] According to another preferred embodiment of the present invention, a program causes a computer to execute a process of acquiring point cloud data measured at a plurality of irradiation positions by a measurement device that performs measurement by receiving reflected light of irradiated light, generating integrated point cloud data by integrating the point cloud data in which information on the irradiation position and reflection intensity is associated with each measurement point, and estimating reflection characteristics representing a relationship between irradiation angle and reflection intensity for each cluster of measurement points indicated by the integrated point cloud data. By executing this program, the computer can preferably estimate the reflection characteristics of an object represented by a cluster of measurement points. Preferably, the program is stored in a storage medium. EXAMPLES

[0020] Hereinafter, preferred embodiments of the present invention will be described with reference to the drawings.

[0021] <First Example> (1) System Overview Fig. 1 is a schematic diagram of a reflection characteristic estimation system according to Example 1. The reflection characteristic estimation system shown in Fig. 1 is a system that estimates the reflection characteristics of features existing around a road based on measurement data generated by a measurement vehicle traveling on the road, and mainly includes the measurement vehicle and a reflection characteristic estimation device 4.

[0022] The measurement vehicle mainly includes an on-vehicle device 1, a Lidar (Light Detection and Ranging, or Laser Illuminated Detection and Ranging) 2, and a sensor unit 3.

[0023] The lidar (range sensor) 2 measures the distance to an object in the outside world discretely by emitting a pulsed laser in a predetermined angular range in the horizontal and vertical directions, and generates point cloud information consisting of measurement points that indicate the three-dimensional position and reflection intensity of the object. In this case, the lidar 2 has an irradiation unit that irradiates a laser beam while changing the irradiation direction, a light receiving unit that receives the reflected light (scattered light) of the irradiated laser beam reflected by the object, and an output unit that outputs scan data based on the light receiving signal output by the light receiving unit. The scan data is point cloud data, and is generated based on the irradiation direction corresponding to the laser beam received by the light receiving unit and the distance to the object in the irradiation direction, which is specified based on the above-mentioned light receiving signal. Hereinafter, each point that constitutes the point cloud data is also called a "measurement point". The lidar 2 may be installed at any position of the measurement vehicle, or multiple lidars may be installed on the measurement vehicle. Date and time data indicating the measurement date and time (generation date and time) is added to the point cloud data.

[0024] The sensor unit 3 is a group of sensors that generate movement information indicating the time-series position and orientation (travel direction) of the LIDAR 2. The sensor unit 3 includes sensors such as a GPS receiver, an acceleration sensor, a gyro sensor, and an IMU (Inertial Measurement Unit). The GPS receiver may generate highly accurate position information indicating the absolute position (e.g., three-dimensional position of latitude, longitude, and altitude) of the measurement vehicle based on an RTK positioning method (i.e., an interferometric positioning method). The sensor unit 3 may be a sensor provided on the LIDAR 2 so as to directly detect the position and orientation of the LIDAR 2. Date and time data indicating the generation date and time is added to the output data of the sensor unit 3.

[0025] The vehicle-mounted device 1 is electrically connected to the LIDAR 2 and the sensor unit 3 by wire or wirelessly, and stores data generated by the LIDAR 2 and the sensor unit 3. The vehicle-mounted device 1 is also capable of data communication with the reflection characteristic estimation device 4, and transmits measurement data "Im" based on output data from the LIDAR 2 and the sensor unit 3 to the reflection characteristic estimation device 4 at a predetermined timing. Specifically, the vehicle-mounted device 1 transmits the measurement data Im including point cloud data measured by the LIDAR 2 at multiple irradiation positions and movement information of the LIDAR 2 based on the output of the sensor unit 3 to the reflection characteristic estimation device 4. The vehicle-mounted device 1 may be configured as a drive recorder together with the sensor unit 3.

[0026] The vehicle-mounted device 1 may perform a process for appropriately correcting the data output from the LIDAR 2 and the sensor unit 3. For example, the vehicle-mounted device 1 may detect a difference between the reference times in the LIDAR 2 and the sensor unit 3, and correct at least one of the date and time data included in the point cloud data and the movement information based on the time difference so that the date and time data included in the point cloud data and the movement information are synchronized.

[0027] The reflection characteristic estimation device 4 receives measurement data Im including point cloud data and movement information of the LIDAR 2 from the vehicle-mounted device 1, and performs processing such as estimating the reflection characteristics of the features measured by the LIDAR 2 based on the received measurement data Im.

[0028] The configuration of the reflection characteristic estimation system shown in FIG. 1 is an example, and various modifications may be made to the configuration shown in FIG. 1. For example, instead of acquiring the measurement data Im through data communication with the vehicle-mounted device 1, the reflection characteristic estimation device 4 may acquire the measurement data Im by reading the measurement data Im stored in the storage medium by the vehicle-mounted device 1 from the storage medium. In this case, the storage medium is electrically connected to the vehicle-mounted device 1 during measurement of the measurement vehicle, so that the vehicle-mounted device 1 writes the measurement data Im. After the measurement of the measurement vehicle, the storage medium is electrically connected to the reflection characteristic estimation device 4, so that the reflection characteristic estimation device 4 reads the measurement data Im. Alternatively, the lidar 2 and the sensor unit 3 may each hold log data generated in a storage medium independently, and the storage medium may be read by the reflection characteristic estimation device 4 to supply the measurement data Im to the reflection characteristic estimation device 4. In this case, the reflection characteristic estimation system may not have the vehicle-mounted device 1. Also, the reflection characteristic estimation device 4 may be composed of multiple devices. In this case, the reflection characteristic estimation apparatus 4 includes a plurality of apparatuses each executing a process assigned to it in advance, and transmitting necessary data between the apparatuses.

[0029] (2) Configuration of the reflection characteristic estimation device 2 is a block diagram showing the functional configuration of the reflection characteristic estimation device 4. The reflection characteristic estimation device 4 has an interface 41, a memory 42, and a controller 43. These elements are connected to each other via a bus line.

[0030] The interface 41 performs interface operations related to data transmission and reception between the reflection characteristic estimation device 4 and an external device. In this embodiment, the interface 41 receives the measurement data Im generated by the vehicle-mounted device 1. The interface 41 may be a wireless interface such as a network adapter for wireless communication with the vehicle-mounted device 1, or may be a hardware interface for reading out the measurement data Im from a storage medium or the like in which the measurement data Im is stored. The interface 41 may also perform interface operations with various peripheral devices such as an input device, a display device, and a sound output device.

[0031] The memory 42 is composed of various types of volatile and non-volatile memories such as a random access memory (RAM), a read only memory (ROM), a hard disk drive, a flash memory, etc. The memory 42 stores a program for the controller 43 to execute a predetermined process. The program executed by the controller 43 may be stored in a storage medium other than the memory 42.

[0032] The memory 42 also stores point cloud data D1, movement information D2, accumulated point cloud data D3, cluster information D4, reflection characteristic information D5, known physical property information D6, and physical property estimation information D7.

[0033] The point cloud data D1 is point cloud data sequentially measured by the vehicle-mounted device 1 while changing the irradiation position. The movement information D2 is movement information representing the position of the lidar 2 at each measurement time of the point cloud data D1. The point cloud data D1 and the movement information D2 are accumulated based on the measurement data Im received from the vehicle-mounted device 1.

[0034] The integrated point cloud data D3 is point cloud data obtained by integrating (integrating) the point cloud data D1 using a common coordinate system. As described later, information on the irradiation position of the lidar 2 (i.e., the measurement position where the measurement light is emitted) is added to each measurement point of the integrated point cloud data D3.

[0035] The cluster information D4 is information for identifying each cluster (group) obtained by performing a clustering process on the integrated point cloud data represented by the integrated point cloud data D3. The cluster information D4 may be an identification label of a cluster added to each measurement point of the integrated point cloud data D3. In this case, the cluster information D4 is incorporated into the integrated point cloud data D3.

[0036] The reflection property information D5 is information representing the reflection property estimated for each cluster by the reflection property estimation device 4. As described later, the reflection property information D5 is information representing the relationship between the irradiation angle and the reflection intensity. The known physical property information D6 is information relating to the reflection property measured in advance for each object. The physical property estimation information D7 is information relating to the estimation result of the physical property for each cluster estimated by the reflection property estimation device 4 based on the known physical property information D6.

[0037] At least one of the point cloud data D1, movement information D2, integrated point cloud data D3, cluster information D4, reflection property information D5, known physical property information D6, and physical property estimation information D7 may be stored in a storage device external to the reflection property estimation device 4, such as a hard disk connected to the reflection property estimation device 4 via the interface 41. The storage device may be a server device that communicates with the reflection property estimation device 4. The storage device may be composed of multiple devices.

[0038] The controller 43 includes one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), or a tensor processing unit (TPU), and controls the entire reflection characteristic estimation device 4. In this case, the controller 43 executes a program stored in the memory 42 or the like to perform processing related to estimation of reflection characteristics. For example, the controller 43 stores the point cloud data and the movement information of the lidar 2 included in the measurement data Im received from the vehicle-mounted device 1 via the interface 41 as point cloud data D1 and movement information D2, respectively, in the memory 42. In addition, the controller 43 generates integrated point cloud data D3, cluster information D4, reflection characteristic information D5, and physical property estimation information D7 based on the point cloud data D1, the movement information D2, and the known physical property information D6, and stores these in the memory 42. The controller 43 functions as an "acquisition means", an "integration processing means", a "clustering processing means", a "reflection characteristic estimation means", a "physical property estimation means", a computer that executes a program, and the like.

[0039] The process executed by the controller 43 is not limited to being realized by software using a program, but may be realized by any combination of hardware, firmware, and software. The process executed by the controller 43 may be realized by using an integrated circuit programmable by a user, such as a field-programmable gate array (FPGA) or a microcomputer. In this case, the program executed by the controller 43 in this embodiment may be realized by using this integrated circuit. In this way, the controller 43 may be realized by various hardware. Furthermore, the functions of the controller 43 may be realized by the cooperation of multiple computers, for example, using cloud computing technology.

[0040] (3) Estimation of reflectance and physical properties Next, a description will be given of the estimation process of reflection characteristics and physical properties executed by the reflection characteristic estimation device 4. In summary, the reflection characteristic estimation device 4 generates integrated point cloud data D3 including information on the illumination position of the lidar 2 based on the point cloud data D1 and the movement information D2, and estimates the reflection characteristics and corresponding physical properties taking into account the illumination angle for each cluster constituted by the integrated point cloud data D3. As a result, the reflection characteristic estimation device 4 accurately estimates the reflection characteristics and physical properties taking into account the difference in reflection intensity according to the illumination angle.

[0041] (3-1) Functional block Fig. 3 is a block diagram showing a functional configuration of the controller 43 of the reflection characteristic estimation device 4 in this embodiment. As shown in Fig. 3, the controller 43 functionally has an integration processing unit 51, a clustering processing unit 52, a reflection characteristic estimation unit 53, and a physical property estimation unit 54. Note that, in Fig. 3, blocks between which data is exchanged are connected by solid lines, but the combination of blocks between which data is exchanged is not limited to that shown in Fig. 3. The same applies to other functional block diagrams described later.

[0042] The integration processing unit 51 generates integrated point cloud data D3 including information on the irradiation position for each measurement point based on the point cloud data D1 and movement information D2 stored in the memory 42 based on the measurement data Im supplied from the vehicle-mounted device 1. The clustering processing unit 52 performs clustering on the integrated point cloud data D3 to generate cluster information D4 representing clusters (groups) of each measurement point constituting the integrated point cloud data D3.

[0043] The reflection characteristic estimation unit 53 estimates the reflection characteristic of each cluster of the integrated point cloud data D3 specified based on the cluster information D4, taking into account the irradiation angle, and generates reflection characteristic information D5 representing the reflection characteristic of each cluster. The physical property estimation unit 54 estimates the physical property of the object corresponding to each cluster based on the reflection characteristic information D5 and the known physical property information D6, and generates the estimated physical property result as physical property estimation information D7.

[0044] (3-2) Details of the integration processing section The integration processing unit 51 generates integrated point cloud data D3 including information on the irradiation position for each measurement point based on the point cloud data D1 and the movement information D2. Hereinafter, the method of generating the integrated point cloud data D3 by the integration processing unit 51 and the details of the data structure of the integrated point cloud data D3 will be described with reference to Figs. 4(A) to (C) and 5(A) to (C).

[0045] FIG. 4(A) is a diagram showing each measurement point of the point cloud data measured when the lidar 2 is at the position "P1" in a coordinate system with the irradiation position of the lidar 2 as the origin (here, O(0,0,0)). FIG. 4(B) is a diagram showing each measurement point of the point cloud data measured when the lidar 2 is at the position "P2" to which the lidar 2 has transitioned from the position P1 in a coordinate system with the irradiation position of the lidar 2 as the origin (here, O(0,0,0)). FIG. 4(C) is a diagram showing each measurement point of the integrated point cloud data D3 obtained by integrating each point cloud data shown in FIG. 4(A) and FIG. 4(B). Here, each measurement point shown in FIG. 4(A) to (C) is shaded based on the measured reflection intensity, and the darker the shade, the lower the reflection intensity.

[0046] In addition, Figure 5(A) shows the data structure of each measurement point shown in Figure 4(A), Figure 5(B) shows the data structure of each measurement point shown in Figure 4(B), and Figure 5(C) shows the data structure of each measurement point shown in Figure 4(C).

[0047] As shown in Fig. 4(A) and Fig. 4(B), each point cloud data before accumulation is represented by a coordinate system (also called "lidar coordinate system") based on the lidar 2 at the time of measurement with the irradiation position as the origin (here, O(0,0,0)). Also, as shown in Fig. 5(A) and Fig. 5(B), each measurement point of each point cloud data before accumulation includes three-dimensional coordinate values ​​(X coordinate value, Y coordinate value, Z coordinate value) with the irradiation position as the origin, and reflection intensity. Note that the point cloud data shown in Fig. 4(A) includes measurement points A to C, and the point cloud data shown in Fig. 4(B) includes measurement points D to F.

[0048] 4(C), the integrated point cloud data D3 is data that represents measurement points measured at different irradiation positions in a common coordinate system (also called "integrated coordinate system"). For example, the integrated point cloud data D3 is data that represents measurement points measured at different irradiation positions in a unified manner, using a coordinate system (for example, a coordinate system based on latitude, longitude, and altitude) that represents the position of the rider 2 in the movement information D2 as the integrated coordinate system. In this case, the integration processing unit 51 converts the coordinate values ​​of each rider coordinate system of the measurement points of each point cloud data before integration into coordinate values ​​of the integrated coordinate system, using information on the position and orientation of the rider 2 at the time of measurement that is indicated by the movement information D2.

[0049] 5(C), the integration processing unit 51 generates integrated point cloud data D3 in which the three-dimensional coordinate value of the integration coordinate system, the reflection intensity, and the irradiation position are associated with each measurement point. In this case, the integration processing unit 51 associates, for each measurement point, the reflection intensity included in the measurement point before integration and the irradiation position of the lidar 2 used for conversion to the integration coordinate system with the three-dimensional coordinate value of the integration coordinate system converted from the lidar coordinate system. As a result, the integration processing unit 51 generates integrated point cloud data D3 including information on the irradiation position, and causes the reflection characteristic estimation unit 53 to suitably execute estimation processing of the reflection characteristics taking into account the irradiation angle based on the integrated point cloud data D3.

[0050] (3-3) Details of the clustering processing unit The clustering processing unit 52 performs clustering to classify nearby measurement points into the same cluster based on the distance between the measurement points that constitute the integrated point cloud data D3, and generates cluster information D4 that identifies the cluster (group) of each measurement point. As a result, the clustering processing unit 52 appropriately sets a cluster for each object in the integrated point cloud data D3.

[0051] 4(C), the clustering processing unit 52 regards the group of measurement points within a dashed frame 61 as a first cluster, and the group of measurement points including measurement points A to F within a dashed frame 62 as a second cluster. Then, the clustering processing unit 52 generates cluster information D4 indicating the cluster to which each measurement point belongs.

[0052] Here, a specific aspect of the clustering will be described.

[0053] In the first clustering mode, the clustering processor 52 performs clustering based on the three-dimensional coordinate values ​​of each measurement point constituting the integrated point cloud data D3, in which measurement points that are close to each other in distance (distance in the integrated coordinate system) are grouped into the same cluster. In this case, the clustering processor 52 may perform clustering by adopting any clustering method (hierarchical cluster analysis, non-hierarchical cluster analysis).

[0054] In the second aspect of clustering, the clustering processor 52 performs clustering based on both the three-dimensional coordinate values ​​and the reflection intensity of each measurement point constituting the integrated point cloud data D3. In this case, the clustering processor 52 performs clustering based on, for example, the distance between measurement points in a four-dimensional coordinate system in which a coordinate axis of reflection intensity is added to an integrated coordinate system, which is a three-dimensional coordinate system. In another example, after performing provisional clustering based on the first aspect, the clustering processor 52 further performs grouping based on reflection intensity for each provisional cluster. Then, the clustering processor 52 sets each set group as a final cluster.

[0055] After the initial clustering, the clustering processing unit 52 may correct the clustering using the physical property estimation information D7 generated by the physical property estimation unit 54 based on the result of the clustering. For example, for each cluster based on the initial clustering result, the clustering processing unit 52 compares the reflection intensity of each measurement point belonging to the target cluster with the estimation result of the physical property estimation unit 54 for the target cluster. Then, when there is a cluster of measurement points whose physical properties (reflection properties) are different from those estimated by the physical property estimation unit 54, the clustering processing unit 52 regards the cluster of measurement points as a different cluster and divides the target cluster. After that, the reflection property estimation unit 53 estimates the reflection property and the physical property estimation unit 54 estimate the physical properties for the divided clusters.

[0056] In this way, the clustering processor 52 may regard points that have clearly different reflection characteristics from nearby measurement points as being in a different cluster. This allows, for example, tree branches that cover a sign to be suitably separated from the sign cluster, and allows estimation of reflection characteristics and physical properties to be performed.

[0057] (3-4) Details of the reflection characteristic estimation part The reflection characteristic estimation unit 53 estimates the reflection characteristic for each cluster of the integrated point cloud data D3 specified based on the cluster information D4. In this case, the reflection characteristic estimation unit 53 first calculates pairs of irradiation positions and reflection intensities for all measurement points of the integrated point cloud data D3. Then, the reflection characteristic estimation unit 53 counts the calculated pairs of irradiation positions and reflection intensities for each cluster, and generates reflection characteristic information D5 based on the counting results for each cluster.

[0058] Here, a specific example of a method for estimating the irradiation angle of each measurement point will be described. For example, the reflection characteristic estimation unit 53 calculates an approximation plane by three or more measurement points for a cluster to which the target measurement point belongs. Then, the reflection characteristic estimation unit 53 calculates an angle between the calculated approximation plane and a vector connecting the three-dimensional coordinate value indicated by the target measurement point and the irradiation position as the irradiation angle. In another example, the reflection characteristic estimation unit 53 may calculate a normal vector for the target measurement point, and calculate an angle between the calculated normal vector and a vector connecting the three-dimensional coordinate position indicated by the target measurement point and the irradiation position as the irradiation angle.

[0059] The reflection characteristic estimation unit 53 may quantize the irradiation angle by a predetermined quantization number and calculate the reflection intensity for each quantized irradiation angle. In this case, when there are a plurality of measurement points with the same irradiation angle in the same cluster, the reflection characteristic estimation unit 53 calculates a representative value (average value, median value, etc.) of the reflection intensity of these measurement points. The reflection characteristic estimation unit 53 can suitably generate the reflection characteristic information D5 representing the relationship between the irradiation angle and the reflection intensity for each cluster. The reflection characteristic information D5 may be graph information representing the relationship between the irradiation angle and the reflection intensity for each cluster. The reflection characteristic estimation unit 53 may generate the reflection characteristic information D5 by interpolating the value of the reflection intensity for which no irradiation angle exists by any interpolation process. The reflection characteristic estimation unit 53 may calculate, as the reflection characteristic information D5, a function that approximately represents the relationship between the irradiation angle and the reflection intensity, without being limited to the above quantization. In this case, any function approximation method may be used.

[0060] (3-5) Details of the physical property estimation part The physical property estimation unit 54 estimates the physical properties of the object corresponding to each cluster based on the reflection characteristic information D5 and the known physical property information D6, and generates the estimation result of the physical properties as physical property estimation information D7.

[0061] Here, the known physical property information D6 is, for example, information on reflection characteristics that indicates the relationship between the irradiation angle and reflection intensity for each category of object (signboard, tree, vehicle, etc.) that exists around the road or for each category of material of the object (for example, retroreflective material, wood, water, iron, other metals, etc.). In another example, the known physical property information D6 may be information on reflection characteristics that indicates the relationship between the irradiation angle and reflection intensity for each category of different physical properties (for example, first physical property, second physical property, etc.). In this way, the known physical property information D6 is information on reflection characteristics for each category of object, material, physical property, etc. (also referred to as "existing physical property category").

[0062] The physical property estimation unit 54 then estimates an existing physical property category corresponding to each cluster based on, for example, a comparison between the reflection characteristic for each existing physical property category registered in the known physical property information D6 and the reflection characteristic for each cluster indicated by the reflection property information D5.The physical property estimation unit 54 then generates information associating the existing physical property category estimated for each cluster with the corresponding cluster as physical property estimation information D7.

[0063] Here, an example of a method for estimating an existing physical property category will be described. For example, the physical property estimation unit 54 calculates the similarity of the reflection characteristic between each cluster and each existing physical property category registered in the known physical property information D6. The method for calculating the similarity may be any similarity calculation method using a cross-correlation function, a distance in a feature space, or the like. Then, the physical property estimation unit 54 determines the existing physical property category with the highest similarity for each cluster. Then, the physical property estimation unit 54 regards the determined existing physical property category as representing the physical property of the object represented by the target cluster, and generates physical property estimation information D7 in which the determined existing physical property category is associated with the target cluster.

[0064] Fig. 6(A) is a diagram showing the relationship between the measurement points obtained for an object 63 and the irradiation position of the lidar 2. Fig. 6(B) is a diagram showing the relationship between the measurement points obtained for an object 64 and the irradiation position of the lidar 2. In Figs. 6(A) and (B), the higher the reflection intensity, the lighter the measurement point is displayed.

[0065] 6A, in the case of object 63, measurement points are generated that show a substantially uniform reflection intensity independent of the irradiation position and irradiation angle. Therefore, in this case, for example, the cluster corresponding to object 63 has the highest similarity in reflection characteristics to the existing physical property category corresponding to a retroreflective material or a sign made from said material. Therefore, in this case, the physical property estimation unit 54 generates physical property estimation information D7 that shows that the cluster corresponding to object 63 belongs to the existing physical property category corresponding to a retroreflective material or a sign made from said material.

[0066] 6B, in the case of object 64, the reflection intensity from the front direction is the highest, and the reflection intensity decreases as the irradiation angle deviates from the front direction of object 64. Therefore, in this case, for example, the cluster corresponding to object 63 has the highest similarity in reflection characteristics to the existing physical property category corresponding to a material with weak reflection in the side direction or an object made from that material. Therefore, in this case, the physical property estimation unit 54 generates physical property estimation information D7 indicating that the cluster corresponding to object 64 belongs to the existing physical property category corresponding to a material with weak reflection in the side direction or an object made from that material.

[0067] In this way, the physical property estimation unit 54 can estimate the reflection characteristics taking into account the irradiation angle for each cluster, thereby performing highly accurate physical property estimation.

[0068] (4) Processing flow Fig. 7 is an example of a flowchart executed by the reflection characteristic estimation device 4 in the first embodiment. The reflection characteristic estimation device 4 executes the process of the flowchart shown in Fig. 7 for each predetermined road section, for example.

[0069] The reflection characteristic estimation device 4 acquires the point cloud data D1 and the movement information D2 (step S11). In this case, the reflection characteristic estimation device 4 receives, for example, the measurement data Im from the vehicle-mounted device 1, and acquires the point cloud data D1 and the movement information D2 based on the received measurement data Im.

[0070] Next, the reflection characteristic estimation device 4 generates integrated point cloud data D3 including information on the irradiation position of the lidar 2 based on the point cloud data D1 and the movement information D2 (step S12). In this case, the integration processing unit 51 of the reflection characteristic estimation device 4 converts the point cloud data D1 expressed in the lidar coordinate system into an integrated coordinate system based on the movement information D2 and integrates it to generate integrated point cloud data D3.

[0071] Then, the reflection characteristic estimation device 4 performs clustering of the measurement points of the integrated point cloud data D3 (step S13). In this case, the clustering processing unit 52 of the reflection characteristic estimation device 4 performs clustering such that measurement points that are close to each other in the integrated coordinate system or a predetermined feature space are grouped into the same cluster.

[0072] Next, the reflection characteristic estimation device 4 estimates reflection characteristics that indicate the relationship between the irradiation angle and the reflection intensity for each cluster identified in step S13 (step S14). In this case, the reflection characteristic estimation unit 53 of the reflection characteristic estimation device 4 estimates the reflection characteristic for each cluster by tallying up the pairs of the irradiation angle and the reflection intensity calculated for each measurement point of the integrated point cloud data D3 for each cluster.

[0073] Next, the reflection property estimation device 4 estimates physical properties for each cluster based on the reflection property estimated in step S14 (step S15). In this case, the physical property estimation unit 54 of the reflection property estimation device 4 estimates an existing physical property category that best fits each cluster based on a comparison result between the known physical property information D6 and the reflection property for each cluster estimated in step S14.

[0074] As described above, the controller 43 of the reflection characteristic estimation device 4 in the first embodiment acquires point cloud data D1 measured at multiple irradiation positions by the LIDAR 2, which performs measurements by receiving reflected light of irradiated light. Then, the controller 43 generates integrated point cloud data D3 in which information on the irradiation position and reflection intensity is associated with each measurement point. Then, the controller 43 estimates reflection characteristics that represent the relationship between the irradiation angle and reflection intensity for each cluster of measurement points indicated by the integrated point cloud data D3. This allows the reflection characteristic estimation device 4 to suitably estimate the reflection characteristics of an object measured by the LIDAR 2, taking into account the irradiation angle.

[0075] <Second Example> In the second embodiment, in addition to the processing in the first embodiment, the reflection characteristic estimation device 4 performs a simulation of the reflection intensity at each measurement point of the integrated point cloud data D3 when the irradiation position is virtually changed after calculating the physical property estimation information D7.

[0076] 8 is an example of a functional block diagram of the reflection characteristic estimation device 4 in the second embodiment. In the second embodiment, the controller 43 of the reflection characteristic estimation device 4 functionally includes an integration processing unit 51, a clustering processing unit 52, a reflection characteristic estimation unit 53, a physical property estimation unit 54, and a reflection characteristic recalculation unit 55. Hereinafter, the same components as those in the first embodiment are appropriately denoted by the same reference numerals, and the description thereof will be omitted.

[0077] Based on viewpoint information "D8" which is information specifying the irradiation position (herein also referred to as "viewpoint") of the virtual LIDAR 2, the reflection characteristic recalculation unit 55 calculates the reflection intensity at each measurement point obtained when the viewpoint is set as the irradiation position. In this case, the reflection characteristic recalculation unit 55 may set a measurable range based on the maximum measuring distance and field of view angle of the LIDAR 2, and calculate the reflection intensity for each measurement point present within the measurable range. Then, the reflection characteristic recalculation unit 55 outputs information indicating the reflection intensity calculated for each measurement point as reflection characteristic output information "D9".

[0078] In this case, the viewpoint information D8 is information specified by an input signal generated by an input device such as a mouse, a keyboard, or a voice input device connected to the interface 41. In this case, the reflection characteristic recalculator 55 may display a GUI (Graphical User Interface) representing a space or the like for which the viewpoint is to be specified on a display or the like connected by the interface 41, and receive an input specifying the viewpoint.

[0079] Here, a method for calculating the reflection intensity of each measurement point will be described. First, the reflection characteristic recalculation unit 55 calculates the irradiation angle of the target measurement point when the specified viewpoint is set as the irradiation position by a geometric method. In addition, the reflection characteristic recalculation unit 55 recognizes the physical property of the cluster to which the measurement point for which the reflection intensity is to be recalculated belongs (i.e., the reflection characteristic according to the irradiation angle) by referring to the physical property estimation information D7. Then, the reflection characteristic recalculation unit 55 recognizes the reflection intensity corresponding to the calculated irradiation angle based on the reflection characteristic indicated by the physical property estimation information D7 of the target cluster. Note that the reflection characteristic recalculation unit 55 may recognize the reflection characteristic for each irradiation angle of the target cluster by further referring to the known physical property information D6 linked to the existing physical property category of the physical property estimation information D7.

[0080] Thereafter, the reflection characteristic recalculator 55 may, for example, display the calculated reflection characteristic output information D9 on a display or the like connected to the interface 41, or may transmit it to a terminal device that performs a specified display based on the reflection characteristic output information D9.

[0081] In this way, according to the second embodiment, it is possible to suitably simulate the reflection intensity at the measurement point obtained for the specified viewpoint.

[0082] In each of the above-described embodiments, the program can be stored using various types of non-transitory computer readable media and supplied to a controller or the like that is a computer. The non-transitory computer readable medium includes various types of tangible storage media. Examples of the non-transitory computer readable medium include magnetic storage media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical storage media (e.g., magneto-optical disks), CD-ROM (Read Only Memory), CD-R, CD-R / W, and semiconductor memory (e.g., mask ROM, PROM (Programmable ROM), EPROM (Erasable PROM), flash ROM, RAM (Random Access Memory)).

[0083] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. In other words, the present invention naturally includes various modifications and corrections that a person skilled in the art could make in accordance with the entire disclosure, including the claims, and the technical ideas. In addition, the disclosures of the above cited patent documents and the like are incorporated herein by reference. [Explanation of symbols]

[0084] 1 On-vehicle device 2. Rider 3 Sensor unit 4 Reflection characteristics estimation device 41 Interface 42 Memory 43 Controller

Claims

1. an acquisition means for acquiring point cloud data measured at a plurality of irradiation positions by a measurement device that performs measurement by receiving reflected light of irradiated light; an integration processing means for generating integrated point cloud data by integrating the point cloud data into a common coordinate system in which information regarding the irradiation position and the reflection intensity is associated with each measurement point; a clustering processing means for performing clustering so that adjacent measurement points are grouped into the same cluster based on distances between the measurement points constituting the integrated point cloud data; a reflection characteristic estimation means for estimating a reflection characteristic representing a relationship between an irradiation angle and a reflection intensity for each cluster of measurement points represented by the integrated point cloud data generated by the clustering; An information processing device having the above configuration.

2. The information processing apparatus according to claim 1 , further comprising: physical property estimation means for estimating a physical property corresponding to each of the clusters based on the reflection property estimated by the reflection property estimation means.

3. The information processing device according to claim 2 , wherein the physical property estimation means estimates a category related to physical properties to which each of the clusters belongs based on the reflection property estimated by the reflection property estimation means and known physical property information representing reflection properties for each category having different physical properties.

4. 4. The information processing apparatus according to claim 1, wherein the clustering processing means performs the clustering based on the distance and the reflection intensity.

5. The information processing device according to any one of claims 1 to 4, wherein the clustering processing means divides the clusters based on the reflection intensity and on the estimation results of physical properties corresponding to each of the clusters based on the reflection characteristics estimated by the reflection characteristic estimation means.

6. The information processing device according to any one of claims 1 to 5, wherein the reflection characteristic estimation means calculates the irradiation angle for each of the measurement points based on the irradiation position and the measured position indicated by the measurement point, and estimates the reflection characteristic based on the calculated irradiation angle and the reflection intensity.

7. The information processing device according to any one of claims 1 to 6, wherein the integrated processing means generates the integrated point cloud data by converting the point cloud data measured at multiple irradiation positions into the common coordinate system based on movement information of the measuring device when measuring the point cloud data.

8. The information processing device according to any one of claims 1 to 7, further comprising a calculation means for calculating a reflection intensity for each of the measurement points obtained when the viewpoint is set as a virtual irradiation position of the measurement device, based on the reflection characteristics estimated by the reflection characteristic estimation means and viewpoint information specifying a viewpoint.

9. A computer-implemented control method, comprising: Obtaining point cloud data measured at a plurality of irradiation positions using a measuring device that performs measurements by irradiating light and receiving reflected light; generating integrated point cloud data by integrating the point cloud data into a common coordinate system in which information regarding the irradiation position and the reflection intensity is associated with each measurement point; clustering the adjacent measurement points into the same cluster based on the distances between the measurement points constituting the integrated point cloud data; estimating a reflection characteristic representing a relationship between an irradiation angle and a reflection intensity for each cluster of measurement points represented by the integrated point cloud data generated by the clustering; Control methods.

10. Obtaining point cloud data measured at a plurality of irradiation positions using a measuring device that performs measurements by irradiating light and receiving reflected light; generating integrated point cloud data by integrating the point cloud data into a common coordinate system in which information regarding the irradiation position and the reflection intensity is associated with each measurement point; clustering the adjacent measurement points into the same cluster based on the distances between the measurement points constituting the integrated point cloud data; A program that causes a computer to execute a process of estimating reflection characteristics that indicate a relationship between an irradiation angle and reflection intensity for each cluster of measurement points represented by the integrated point cloud data generated by the clustering.

11. A storage medium storing the program according to claim 10.

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