JP7803214B2 - Object recognition method and object recognition device - Google Patents

Description

The present invention relates to an object recognition method and an object recognition device.

Patent Document 1 discloses an object recognition device that identifies the type of target object by estimating the rectangle of the target object from information on the detection pattern of a group of reflection points of the target object detected by an on-board radar. In estimating this rectangle, all reflection points of the detection pattern obtained by clustering are rotated one degree at a time on the horizontal plane on which each reflection point is plotted, with the cluster center as the origin, and the reflection point density in the horizontal direction is projected each time. Then, from the rotated state where the reflection point density value peaks, an axis that forms at least one of the two L-shaped sides of the rectangle is found. If only one axis is found, the other axis that forms the remaining L-shaped side is set in a direction perpendicular to the axis that forms the original axis, thereby detecting the axis that forms the width or depth of the rectangle.

JP 2012-52838 A

However, if there is a reflection point within the contour of the target object on a horizontal plane (top view), the peak density value may deviate from the edge that corresponds to the actual width or depth of the target object. In particular, if the target object is tilted relative to the horizontal direction, the reflection points on two L-shaped sides of the target object may overlap, causing the peak density value to deviate from the edge that corresponds to the actual width or depth of the target object. This may result in an incorrect detection of the axis that corresponds to the width or depth of the rectangle, reducing the accuracy of estimating the target object's orientation.

The present invention aims to improve the accuracy of pose estimation of a target object.

An object recognition method and object recognition device according to one aspect of the present invention comprises a ranging sensor mounted on a moving body that generates ranging point data relating to ranging points on the surface of a target object present around the moving body, and a controller that recognizes the target object based on the ranging point data acquired from the ranging sensor. The controller extracts, from the ranging point data, multiple ranging point data on the surface of the same target object as a ranging point cloud, transforms the ranging point cloud into a two-dimensional or three-dimensional coordinate system with the position of the ranging sensor as the origin, sets multiple model areas including all of the ranging point clouds on the two-dimensional or three-dimensional coordinate system, sets areas on the two-dimensional or three-dimensional coordinate system that are blind spots from the ranging sensor due to the ranging point clouds as occupied areas, and sets areas on the two-dimensional or three-dimensional coordinate system other than the occupied areas as non-occupied areas, selects from the multiple model areas model areas where the area or volume of an overlapping area where each of the model areas and the non-occupied area overlaps is equal to or smaller than a predetermined value, and recognizes the selected model area as the target object.

This invention improves the accuracy of estimating the pose of a target object.

FIG. 1 is a diagram showing the configuration of an object recognition device according to an embodiment of the present invention. FIG. 2 is a diagram for explaining an example of a method for converting distance measurement point data on the surface of another vehicle into two-dimensional distance measurement point cloud data viewed from above the moving body. FIG. 3 is a diagram showing distance measurement point data between a nearby vehicle and a wall, and between the vehicle and the surface of the wall. FIG. 4 is a diagram illustrating an example of a method for setting a model region. FIG. 5 is a diagram for explaining a method for setting an unoccupied area. FIG. 6 is a diagram illustrating a method for selecting a model region. FIG. 7 is a flowchart showing the operation of the object recognition device according to the first embodiment. FIG. 8 is a flowchart showing the operation of the object recognition device according to the second embodiment.

(First embodiment)
A first embodiment of the present invention will be described below with reference to the drawings. In the description, the same components are designated by the same reference numerals and redundant description will be omitted.

An example configuration of the object recognition device 1 according to the first embodiment will be described with reference to Figure 1. As shown in Figure 1, the object recognition device 1 includes a distance measurement sensor 10 and a control unit 20 consisting of a distance measurement point acquisition unit 21, a clustering processing unit 22, a surrounding object recognition unit 23, and a driving assistance unit 24.

The ranging sensor 10 is mounted on a mobile body and generates ranging point data relating to ranging points on the surface of target objects present around the mobile body. The ranging sensor 10 transmits probe waves around the mobile body to scan the periphery of the mobile body and receives the reflected waves from the surfaces of target objects present around the mobile body. Based on the received reflected waves, the ranging sensor 10 calculates the positions of the reflection points where the probe waves are reflected at multiple positions on the surface of the target object as the relative positions of the reflection points with respect to the ranging sensor 10 of the mobile body, and obtains ranging point data indicating the relative position of each reflection point.

One example of the distance measurement sensor 10 is a LIDAR (Laser Imaging Detection and Ranging). LIDAR measures the distance and direction to an object and recognizes the shape of the object by emitting light (laser light) at a surrounding object and measuring the time it takes for that light (reflected light) to hit the object and bounce back. LIDAR can also obtain the positional relationship of objects in three dimensions. It is also possible to perform mapping using the intensity of the reflected light.

The ranging point data includes position information of the ranging point. The position information of the ranging point is information indicating the position coordinates of the ranging point. The position coordinates use a three-dimensional coordinate system expressed by x, y, and z coordinates, with the installation position of the ranging sensor 10 as the origin. The position coordinates may also use a polar coordinate system expressed by the direction from the ranging sensor 10 toward the ranging point (yaw angle, pitch angle) and the distance (depth) from the ranging sensor 10 to the ranging point. Furthermore, the ranging point data is acquired at predetermined intervals.

The ranging sensor 10 can be any sensor that can acquire the positions of multiple ranging points on the surface of a target object as ranging point data, and is not limited to the above. For example, the ranging sensor 10 may be a stereo camera mounted on a moving object. When a stereo camera is used for the ranging sensor 10, it is possible to acquire camera images from at least two different viewpoints, determine the parallax from corresponding positions between the images, and acquire three-dimensional information for each position in the images using the principles of triangulation. The ranging point data may also be acquired from the positions and distances of pixels corresponding to surrounding objects in images captured by the stereo camera around the moving object. The ranging sensor 10 outputs the acquired ranging point data to the ranging point acquisition unit 21 of the control unit 20.

The control unit 20 is a general-purpose microcomputer equipped with a CPU (Central Processing Unit), memory, and input/output units. A computer program for functioning as the object recognition device 1 is installed in the microcomputer. By executing the computer program, the microcomputer functions as multiple information processing circuits equipped in the object recognition device 1. The control unit 20 processes data acquired from the distance measurement sensor 10.

Note that while this example shows the implementation of multiple information processing circuits provided in the object recognition device 1 using software, it is of course also possible to configure the information processing circuits by providing dedicated hardware for executing each of the information processes described below. Furthermore, the multiple information processing circuits may also be configured using separate hardware.

The control unit 20 recognizes target objects based on the ranging point data acquired by the ranging sensor 10. The control unit 20 includes a ranging point acquisition unit 21, a clustering processing unit 22, a surrounding object recognition unit 23, and a driving assistance unit 24 as examples of multiple information processing circuits (information processing functions).

Each information processing function of the control unit 20 will be described below.
The ranging point acquisition unit 21 acquires ranging point data from the ranging sensor 10. The ranging point data acquired by the ranging sensor 10 includes various ranging point data of road surfaces and the like other than objects. Therefore, the ranging point acquisition unit 21 first extracts ranging point data of three-dimensional objects from the acquired ranging point data.

The method for extracting distance measurement point data for a three-dimensional object may involve extracting distance measurement point data at a height equal to or greater than a first predetermined height value from the road surface. For example, the first predetermined height value can be set to the height of the bottom of a typical vehicle's bumper on the z axis of a three-dimensional coordinate system. The method for extracting distance measurement point data for a three-dimensional object may also involve extracting distance measurement point data at a height equal to or greater than a first predetermined height value and less than a second predetermined height value from the road surface. For example, the second predetermined height value can be set to include the type of parked vehicle (large vehicle, standard car, etc.) recognized by a camera or the like, up to the roof height corresponding to the type of vehicle. Alternatively, a grid map storing distance measurement point data may be generated to determine the distance measurement points between the road surface and a three-dimensional object.

Next, the clustering processing unit 22 performs clustering processing to extract ranging point data for the extracted three-dimensional object. Here, clustering is a technique for extracting, from the ranging point data for the extracted three-dimensional object, multiple ranging point data on the surface of the same target object as a cluster (ranging point cloud). As an example, from the multiple ranging point data for the extracted three-dimensional object, ranging point data where the distance between two points is less than a predetermined value is extracted as a ranging point cloud. For example, when the target object is a vehicle (another vehicle), the extracted ranging point cloud will look like (a) in Figure 2.

Figure 3 shows distance measurement points between a nearby vehicle and a wall, and between the vehicle and the surface of the wall. When a vehicle is close to a wall, the distance measurement points of multiple target objects, such as the vehicle and the wall, may be erroneously extracted as a distance measurement point cloud of the same target object. In such cases, the clustering processing unit 22 reduces the predetermined value of the distance between the two points used to extract the distance measurement point cloud from the previous value, performs clustering processing again, and re-extracts the distance measurement point cloud. In other words, the clustering processing unit 22 divides the distance measurement point cloud. The specific flow will be described later using the flowchart in Figure 7.

Next, the clustering processing unit 22 converts the extracted ranging point cloud into two-dimensional ranging point cloud data viewed from above on the moving object, as shown in Figure 2(b). Here, two-dimensional ranging point cloud data viewed from above on the moving object refers to ranging point cloud data converted into two-dimensional coordinates by projecting the ranging point cloud onto a two-dimensional coordinate system expressed by x and y coordinates with the position of the ranging sensor 10 as the origin, and the same meaning will be used hereinafter. Note that the clustering processing unit 22 may also project the ranging point cloud onto a three-dimensional coordinate system expressed by x, y, and z coordinates with the position of the ranging sensor as the origin, and convert it into three-dimensional coordinates.

Next, the surrounding object recognition unit 23 sets a model region for the target object based on the two-dimensional ranging point cloud data input from the clustering processing unit 22. The surrounding object recognition unit 23 sets multiple model regions that include all of the ranging point clouds on two-dimensional coordinates. Since most objects in a traffic environment (cars, motorcycles, bicycles, etc.) have shapes that are close to rectangular when viewed from above, the surrounding object recognition unit 23 sets a rectangular region that approximates the rectangular shape of the target object as a model region, based on the assumption that the target object is rectangular.

When the ranging point cloud is converted into three-dimensional coordinates, the surrounding object recognition unit 23 may set a rectangular parallelepiped region that approximates the target object in three-dimensional coordinates as a model region, based on the assumption that the target object has a rectangular parallelepiped shape. In other words, the surrounding object recognition unit 23 sets multiple model regions that include all of the ranging point cloud in two-dimensional or three-dimensional coordinates.

As an example of a method for setting a model area, the surrounding object recognition unit 23 may set rectangular areas (model areas) 30a, 30b, and 30c of the smallest area that include all of the ranging point group, with different angles θ between the long side direction of the rectangle and the traveling direction of the target object V2, as shown in Figures 4(a), (b), and (c). In Figure 4(b), the angle θ between the long side direction of the rectangle and the traveling direction of the target object V2 is 0°. For example, the surrounding object recognition unit 23 may set multiple model areas of the smallest area that include all of the ranging point group, with the angle θ between the long side direction of the rectangle and the traveling direction of the target object varying in 1° increments from 0° to 89°. In this case, 90 different rectangular areas with angles θ between the long side direction of the rectangle and the traveling direction of the target object ranging from 0° to 89° are set as model areas. The number of degrees at which model areas are set is not limited to the above. In this embodiment, an example is shown in which a model region with the smallest area that includes all of the ranging points is set, but a model region that includes only some of the ranging points may also be set.

When the ranging point cloud is converted into three-dimensional coordinates, the surrounding object recognition unit 23 may set multiple model areas of the smallest volume that include all of the ranging point cloud in the three-dimensional coordinates, where the angle between the long side direction of the rectangular parallelepiped on the x-y plane and the direction of travel of the target object is different. In this case, 90 different rectangular parallelepiped areas, where the angle between the long side direction of the rectangular parallelepiped on the x-y plane and the direction of travel of the target object ranges from 0° to 89°, may be set as model areas.

In FIG. 5, the distance measurement sensor 10 is mounted on a mobile object V1, and a distance measurement point cloud of a target object V2 has been extracted. The surrounding object recognition unit 23 sets the area that is a blind spot from the distance measurement sensor 10 (origin) based on the distance measurement point cloud as an occupied area 40 on a two-dimensional coordinate system with the position of the distance measurement sensor 10 as the origin, as shown in FIG. 5. The surrounding object recognition unit 23 also sets the area other than the occupied area 40 as a non-occupied area 50 on a two-dimensional coordinate system with the position of the distance measurement sensor 10 as the origin. Note that when the distance measurement point cloud is converted into three-dimensional coordinates, the surrounding object recognition unit 23 may set the area that is a blind spot from the distance measurement sensor 10 (origin) based on the distance measurement point cloud as an occupied area on a three-dimensional coordinate system with the position of the distance measurement sensor 10 as the origin, and set the area other than the occupied area as a non-occupied area on the three-dimensional coordinate system.

Here, the occupied area is the area where the detection wave is blocked by the ranging point group when the detection wave is applied, with the position of the ranging sensor 10 as the origin. Note that the ranging point groups are considered to have continuous points. The occupied area is set based on the ranging point in the ranging point group that is the shortest distance from the ranging sensor 10 in each direction from the ranging sensor 10.

The surrounding object recognition unit 23 calculates the area of the overlapping region where each of the multiple model regions overlaps with the non-occupied region 50. For example, the area of the overlapping region where the model region 30a in FIG. 4(a) overlaps with the non-occupied region 50 is the area of the shaded region Ra in FIG. 6(a). The area of the overlapping region where the model region 30b in FIG. 4(b) overlaps with the non-occupied region 50 does not exist as shown in FIG. 6(b), so the area of the overlapping region is 0. The area of the overlapping region where the model region 30c in FIG. 4(c) overlaps with the non-occupied region 50 is the area of the shaded region Rc in FIG. 6(c). Note that when the ranging point cloud is converted into three-dimensional coordinates, the surrounding object recognition unit 23 calculates the volume of the overlapping region where each of the multiple model regions overlaps with the non-occupied region.

The surrounding object recognition unit 23 selects a model area where the area or volume of the overlapping area is less than a predetermined value. If there are multiple model areas where the area or volume of the overlapping area is less than a predetermined value, the surrounding object recognition unit 23 may select the model area where the area or volume of the overlapping area is smallest. For example, in Figure 6, model area 30b (not shown) where the area of the overlapping area is smallest (0) may be selected. Note that the method of selecting a model area is not limited to the above method, and the surrounding object recognition unit 23 may select a model area where the area or volume of the overlapping area where each of the multiple model areas overlaps with the occupied area is greater than or equal to a predetermined value.

The surrounding object recognition unit 23 recognizes the selected model area as a target object existing around the moving body. In other words, it recognizes the position, shape, and posture of the selected model area as the position, shape, and posture of the target object. Furthermore, if the width of the model area is close to the width of the vehicle, that width may be estimated as the front or rear of the vehicle. Furthermore, by using map information, etc., it is possible to estimate whether the vehicle is facing forward or backward from the lane in which it is located. The attributes of the recognized target object can be determined based on at least one of the size and shape of the model area. For example, the vehicle type (whether it is a truck or a passenger car) can be determined based on the size of the model area.

The driving assistance unit 24 provides optimal driving assistance to a moving vehicle equipped with a distance measurement sensor 10 based on information about the target object recognized by the surrounding object recognition unit 23. For example, if the driving assistance unit 24 determines that another vehicle is approaching the vehicle when the distance measurement sensor 10 is installed in the vehicle, it controls various actuators such as the steering actuator, accelerator pedal actuator, and brake actuator of the vehicle to stop or decelerate the vehicle, or provides driving assistance that involves evasive steering. In this case, the accuracy of estimating the vehicle type of the target object is improved, thereby improving the accuracy of driving assistance.

Next, an example of the operation of the object recognition device 1 according to the first embodiment will be described with reference to the flowchart in Figure 7.

In step S101 of FIG. 7, the ranging sensor 10 generates ranging point data relating to ranging points on the surface of a target object present around the moving body. The ranging sensor 10 outputs the generated ranging point data to the ranging point acquisition unit 21. The ranging point acquisition unit 21 acquires the ranging point data from the ranging sensor 10.

In step S102, the ranging point acquisition unit 21 extracts ranging point data of the three-dimensional object from the ranging point data acquired in step S101.

In step S103, the clustering processing unit 22 performs clustering processing on the ranging point data of the three-dimensional object extracted in step S102, and extracts ranging point clouds of the same target object. From the ranging point data of the three-dimensional object, the clustering processing unit 22 extracts multiple ranging point data on the surface of the same target object as a ranging point cloud.

In step S104, the clustering processing unit 22 converts the ranging point cloud extracted in step S103 into two-dimensional coordinates with the position of the ranging sensor 10 as the origin. In other words, the ranging point cloud extracted in step S103 is converted into two-dimensional ranging point cloud data.

In step S105, the surrounding object recognition unit 23 sets multiple model areas based on the two-dimensional ranging point cloud data generated in step S104. The surrounding object recognition unit 23 sets multiple model areas with the smallest area that include all of the ranging point cloud on the two-dimensional coordinate system. For example, on the two-dimensional coordinate system, the unit sets as model areas rectangular areas with the smallest area that include all of the ranging point cloud for each 1° angle between the long side direction of the rectangle and the direction of travel of the target object from 0° to 89°.

In step S106, the surrounding object recognition unit 23 sets an occupied area based on the two-dimensional ranging point cloud data acquired in step S104. On a two-dimensional coordinate system with the position of the ranging sensor 10 as the origin, the surrounding object recognition unit 23 sets the area that is a blind spot from the ranging sensor 10 (origin) based on the ranging point cloud as the occupied area.

In step S107, the surrounding object recognition unit 23 sets a non-occupied area based on the two-dimensional ranging point cloud data acquired in step S104. The surrounding object recognition unit 23 sets an area other than the occupied area as a non-occupied area on a two-dimensional coordinate system with the position of the ranging sensor 10 as the origin.

In step S108, the surrounding object recognition unit 23 calculates the area of the overlapping region where each of the multiple model regions obtained in step S105 overlaps with the unoccupied region obtained in step S107.

In step S109, the surrounding object recognition unit 23 determines whether a model area exists in which the area of the overlapping area is equal to or less than a predetermined value. If a model area exists in which the area of the overlapping area is equal to or less than the predetermined value, the process proceeds to step S111; if not, the process proceeds to step S110.

In step S110, the clustering processing unit 22 divides the ranging point cloud. Specifically, the clustering processing unit 22 reduces the predetermined value of the distance between the two points from which the ranging point cloud was extracted from compared to the previous time. Then, the flow returns to step S104. In other words, if there is no model area where the area or volume of the overlapping area is equal to or less than the predetermined value, the clustering processing unit 22 divides the ranging point cloud and re-extracts ranging points on the surface of the same target object from the divided ranging point cloud as a ranging point cloud.

In step S111, the surrounding object recognition unit 23 determines whether there are multiple model areas where the area of the overlapping region is equal to or less than a predetermined value. If there are multiple model areas where the area of the overlapping region is equal to or less than the predetermined value, the process proceeds to step S113. If there are no multiple model areas where the area of the overlapping region is equal to or less than the predetermined value, the process proceeds to step S112. In step S112, the surrounding object recognition unit 23 selects a rectangular area where the area of the overlapping region is equal to or less than the predetermined value.

In step S113, the surrounding object recognition unit 23 selects the model region with the smallest overlapping area.

In step S114, the surrounding object recognition unit 23 recognizes the model area selected in step S112 or step S113 as the target object. The control unit 20 ends the processing of FIG. 7, and the driving assistance unit 24 provides optimal driving assistance to the mobile object on which the ranging sensor 10 is mounted, based on the information about the target object recognized in step S114.

(Effects of the first embodiment)
(1) In the first embodiment, a target object is recognized based on ranging point data related to ranging points on the surface of a target object existing around the mobile object, which data is generated by a ranging sensor 10 mounted on the mobile object. Among the ranging point data, multiple ranging point data on the surface of the same target object are extracted as a ranging point cloud. The ranging point cloud is converted into a two-dimensional or three-dimensional coordinate system with the position of the ranging sensor as the origin. Multiple model areas including all of the ranging point clouds are set in the two-dimensional or three-dimensional coordinate system, and areas blind spots from the ranging sensor 10 due to the ranging point clouds are set as occupied areas, and areas other than the occupied areas are set as unoccupied areas. From the multiple model areas, model areas are selected in which the area or volume of the overlapping area where each model area and the unoccupied area overlap is equal to or less than a predetermined value, and the selected model area is recognized as the target object.

Of the multiple model areas set, those whose overlapping area with unoccupied areas has an area or volume less than a predetermined value can be selected and recognized as the target surrounding object. This allows the selection of model areas from the multiple model areas set from the ranging point data whose deviation from the target object is less than a predetermined value. By recognizing the selected model area as the target object, the accuracy of estimating the target object's posture can be improved.

(2) In the first embodiment, on a two-dimensional coordinate system or a three-dimensional coordinate system, an occupied area is set based on the ranging point among the ranging points that has the shortest distance from the ranging sensor 10 in each direction from the ranging sensor 10.
The ranging point at which the distance from the ranging sensor 10 is shortest in each direction from the ranging sensor is the ranging point on the contour of the target object, so the occupied area can be set based on the ranging point on the contour of the target object. This allows the occupied area to be set accurately without being affected by ranging points inside the contour of the target object, and the unoccupied area to be set accurately, thereby improving the accuracy of estimating the orientation of the target object.

(3) In the first embodiment, if there are multiple model regions whose overlapping areas have an area or volume equal to or less than a predetermined value, the model region with the smallest overlapping area or volume is selected and recognized as the target object.
This allows the optimum model region to be selected, and the posture of the target object can be estimated based on the optimum model region.

(4) In the first embodiment, if there is no model area where the area of the overlapping area is less than a predetermined value, the ranging point group is divided, and from the divided ranging point group, ranging points on the surface of the same target object are re-extracted as the ranging point group.
This allows the clustering of distance measurement points for a plurality of target objects to be redone even if the distance measurement points are mistakenly clustered as a single target object distance measurement point cloud.

Second Embodiment
A second embodiment of the present invention will be described below. The second embodiment aims to stably maintain the estimation accuracy of the posture of a target object even when the estimation accuracy of the model region decreases. Note that elements similar to those in the first embodiment are given the same reference numerals, and their description will be omitted. Differences between the second embodiment of the present invention and the first embodiment will be described below.

In the object recognition device 1 according to the first embodiment, when there are multiple model regions whose overlapping area or volume is less than a predetermined value, the surrounding object recognition unit 23 selects the model region whose overlapping area is the smallest in area or volume. However, in the object recognition device 1 according to the second embodiment, when there are multiple model regions whose overlapping area or volume is less than a predetermined value, the surrounding object recognition unit 23 selects a model region whose angular difference with the model region selected when the target object was recognized one frame earlier is less than a predetermined angular difference, and recognizes it as the target object. In other words, when there are multiple model regions whose overlapping area or volume is less than a predetermined value, the surrounding object recognition unit 23 selects a rectangular region whose angular difference with the model region selected during the previous processing and recognized as the target object is less than a predetermined angular difference.

FIG. 8 is a flowchart illustrating an example of the operation of the object recognition device 1 according to the second embodiment.
As shown in the figure, in this embodiment, the processes of steps S201 to S212 and step S214 in FIG. 8 are the same as the processes of steps S101 to S112 and step S114 in FIG. 7, and therefore description thereof will be omitted. The process of step S213 in FIG. 8 differs from the first embodiment. The object recognition device 1 according to this embodiment repeatedly executes a series of processes consisting of steps S201 to S214 shown in FIG. 8 at a predetermined cycle. The unit of repeatedly executed processes is called a "frame." In other words, the object recognition device 1 according to the second embodiment repeatedly executes the frames shown in FIG. 8, and selects a model region to be recognized as a target object for each frame.

In step S213, the surrounding object recognition unit 23 selects a model area whose angular difference from the model area selected during the previous processing and recognized as the target object is less than or equal to a predetermined angular difference.

(Effects of the second embodiment)
(5) In the second embodiment, if there are multiple model regions whose overlapping areas have an area or volume that is less than a predetermined value, a model region whose angular difference with the model region selected when the target object was recognized one frame earlier is less than a predetermined angular difference is selected and recognized as the target object.
This makes it possible to stably maintain the accuracy of estimating the posture of the target object even if the estimation accuracy of the model region drops momentarily.

10 Distance measuring sensor 20 Control unit (controller)
21 Range-finding point acquisition unit 22 Clustering processing unit 23 Surrounding object recognition unit 24 Driving assistance unit

Claims (6)

a distance measurement sensor mounted on a moving body and generating distance measurement point data relating to distance measurement points on the surface of a target object present around the moving body;
a controller that recognizes the target object based on the distance measurement point data acquired from the distance measurement sensor,
The controller
extracting a plurality of pieces of distance measurement point data on the surface of the same target object from the distance measurement point data as a distance measurement point group;
converting the range-finding point cloud into two-dimensional or three-dimensional coordinates with the position of the range-finding sensor as the origin;
setting a plurality of model regions including all of the distance measurement point clouds on the two-dimensional coordinate system or the three-dimensional coordinate system;
On the two-dimensional coordinate system or the three-dimensional coordinate system, a region that is a blind spot from the distance measurement sensor due to the distance measurement point cloud is set as an occupied region;
On the two-dimensional coordinate system or the three-dimensional coordinate system, an area other than the occupied area is set as an unoccupied area;
selecting, from among the plurality of model regions, model regions in which the area or volume of an overlapping region where each of the model regions and the unoccupied region overlap is equal to or less than a predetermined value;
An object recognition method, comprising: recognizing the selected model region as the target object. The controller
The object recognition method described in claim 1, characterized in that the occupied area is set on the two-dimensional coordinate system or the three-dimensional coordinate system based on the ranging point among the ranging points that has the shortest distance from the ranging sensor in each direction from the ranging sensor. The controller
3. The object recognition method according to claim 1, wherein, when there are a plurality of model regions each having an area or volume of the overlapping region that is equal to or less than the predetermined value, the model region having the smallest area or volume of the overlapping region is selected and recognized as the target object. The controller
The object recognition method described in claim 1 or 2, characterized in that if there is no model area where the area or volume of the overlapping area is less than the predetermined value, the ranging point cloud is divided, and ranging points on the surface of the same target object from the divided ranging point cloud are re-extracted as the ranging point cloud. The controller
3. The object recognition method according to claim 1, wherein, when there are a plurality of model regions in which the area or volume of the overlapping region is equal to or less than the predetermined value, the model region in which the angular difference between the model region selected when the target object was recognized one frame earlier is equal to or less than the predetermined angular difference is selected, and the model region is recognized as the target object. a distance measurement sensor mounted on a moving body and generating distance measurement point data relating to distance measurement points on the surface of a target object present around the moving body;
a controller that recognizes the target object based on the distance measurement point data acquired from the distance measurement sensor,
extracting a plurality of pieces of distance measurement point data on the surface of the same target object from the distance measurement point data as a distance measurement point group;
converting the range-finding point cloud into two-dimensional or three-dimensional coordinates with the position of the range-finding sensor as the origin;
setting a plurality of model regions including all of the distance measurement point clouds on the two-dimensional coordinate system or the three-dimensional coordinate system;
On the two-dimensional coordinate system or the three-dimensional coordinate system, a region that is a blind spot from the distance measurement sensor due to the distance measurement point cloud is set as an occupied region;
On the two-dimensional coordinate system or the three-dimensional coordinate system, an area other than the occupied area is set as an unoccupied area;
selecting, from among the plurality of model regions, model regions in which the area or volume of an overlapping region where each of the model regions and the unoccupied region overlap is equal to or less than a predetermined value;
An object recognition device that recognizes the selected model region as the target object.