WO2018221458A1

WO2018221458A1 - Updating device, control method, program, and storage medium

Info

Publication number: WO2018221458A1
Application number: PCT/JP2018/020370
Authority: WO
Inventors: 和紀小山; 加藤　正浩
Original assignee: パイオニア株式会社
Priority date: 2017-05-31
Filing date: 2018-05-28
Publication date: 2018-12-06

Abstract

A server device 2 receives, from onboard devices 1 of a plurality of vehicles, matching decrease information D1 relating to voxels for which the degree of matching between point group data measured by a lidar 30 and point group data indicated by voxel data included in a map DB10 is low. The server device 2 then determines an object voxel Btag as a voxel to be updated on the basis of the matching decrease information D1, and updates the voxel data of the object voxel Btag on the basis of measurement data for which an overall evaluation function E is high.

Description

Update device, control method, program, and storage medium

The present invention relates to a technique for updating a map.

Conventionally, a technique for updating map data based on the output of a sensor installed in a vehicle is known. For example, Patent Document 1 discloses that difference information representing a difference between a position of a moving object and a traveling position of the moving object on map data is associated with the position information and stored in the difference database. A map data update system configured to set information indicating whether or not to update the map data corresponding to the position information based on the difference information corresponding to the position information stored in is disclosed.

Japanese Unexamined Patent Publication No. 2016-180980

In the map data for the purpose of automatic driving or the like, there is a case where information of a point cloud measured by a distance measuring sensor or the like is stored in order to grasp a detailed position of a stationary structure around the road. Patent Document 1 does not disclose how to update the information of the point cloud stored in the map data.

The present invention has been made to solve the above-described problems, and has as its main object to provide an update device that can suitably update a map including point cloud information.

The invention according to claim 1 is an update device, which has a difference greater than or equal to a predetermined value from map point cloud information assigned to each of a plurality of areas of map information, and is based on a reference position measured by a measurement unit Based on an acquisition unit that acquires measurement point cloud information about each measurement distance to a plurality of positions from a plurality of moving bodies, and a matching result of the measurement point group information and map point group information for each of the plurality of moving bodies And an update unit that updates information of a region that satisfies a predetermined condition among the plurality of regions.

The invention according to claim 9 is a control method executed by the updating device, and has a difference greater than or equal to a predetermined value from the map point cloud information assigned to each of the plurality of areas of the map information, and is measured by the measurement unit. In addition, an acquisition step of acquiring measurement point group information regarding each measurement distance from the reference position to a plurality of positions from a plurality of moving bodies, and the measurement point group information and map point group information for each of the plurality of moving bodies. And updating the information of the region satisfying a predetermined condition among the plurality of regions based on the collation result.

The invention according to claim 10 is a program executed by a computer, and has a map point cloud information given for each of a plurality of areas of map information and a difference of a predetermined value or more, and is measured by a measurement unit. An acquisition unit that acquires measurement point cloud information related to each measurement distance from a reference position to a plurality of positions from a plurality of moving bodies, and verification of the measurement point group information and map point group information for each of the plurality of moving bodies Based on the result, the computer is caused to function as an update unit that updates information of a region satisfying a predetermined condition among the plurality of regions.

It is a schematic structure of a driving assistance system. The block configuration of a vehicle equipment and a server apparatus is shown. An example of the schematic data structure of voxel data is shown. A specific example of NDT scan matching will be described. A specific example of NDT scan matching in which a weighting value is set for each voxel will be described. A specific example of NDT scan matching related to the change of the weighting value will be shown. It is a flowchart which shows the process sequence which an onboard equipment performs. It is a flowchart which shows the process sequence which a server apparatus performs.

According to a preferred embodiment of the present invention, the update device has a difference greater than or equal to a predetermined value from the map point cloud information assigned to each of the plurality of areas of the map information, and is based on the reference position measured by the measurement unit. Based on an acquisition unit that acquires measurement point cloud information about each measurement distance to a plurality of positions from a plurality of moving bodies, and a matching result of the measurement point group information and map point group information for each of the plurality of moving bodies And an update unit that updates information of a region that satisfies a predetermined condition among the plurality of regions. Here, “having a difference greater than or equal to a predetermined value” means that an index (for example, an evaluation value) indicating the degree of matching is obtained when matching between the target map point cloud information and the measurement point cloud information is performed. It means that it becomes below a predetermined value. The predetermined value corresponds to a threshold value for determining whether or not the map point group information and the measurement point group information are deviated, and is set based on, for example, an experiment. According to this aspect, the update device can preferably update the information for the region where it is estimated that the static structure is changed.

In another aspect of the updating device, the updating unit determines a region that satisfies the predetermined condition based on a result of collation of the measurement point group information and map point group information, and the measurement point group corresponding to the region. Based on the information, the information of the area is updated. According to this aspect, the update device can determine an area that needs to be updated, and suitably update the information of the area.

In another aspect of the updating device, the updating unit determines a region where the evaluation values for each of the plurality of regions calculated from the matching result of the measurement point group information and the map point group information are similar to each other with the predetermined condition. Determine as the area to fill. According to this aspect, the update device is an area where the map point cloud information and the measurement point cloud information are separated from each other, and the area where the evaluation value indicating the matching result is similar is the area where the static structure has changed. Therefore, the data in the area can be updated appropriately.

In another aspect of the updating apparatus, the updating unit updates information on a region that satisfies the predetermined condition based on measurement point group information in which an evaluation value calculated from the collation result is higher than a predetermined value. According to this aspect, the update device can accurately update the region information based on the highly reliable measurement point cloud information.

In another aspect of the updating apparatus, the updating unit sets a region where the number of the measurement point group information for each of the plurality of regions acquired by the acquiring unit is a predetermined number or more as a region that satisfies the predetermined condition. decide. According to this aspect, the update device can specify a region where the measurement point group information and the map point group information are separated from each other by a statistical method, and can appropriately update the information of the region.

In another aspect of the update device, the update device further includes a storage unit that stores map information for distribution including map point cloud information for each of the plurality of areas, and the update unit satisfies the predetermined condition. The map point cloud information of the distribution map information corresponding to the area is updated. According to this aspect, the update device can suitably update the map point cloud information for each area included in the map information stored for distribution.

In another aspect of the update device, the distribution map information includes reliability information related to reliability for each of the plurality of regions, and the update unit acquires the measurement point acquired by the acquisition unit. When the number of group information is larger, the reliability indicated by the reliability information for the area is lowered, and the map point group information of the distribution map information corresponding to the area satisfying the predetermined condition is updated. Initialize reliability information. According to this aspect, the update device can accurately update the reliability information for each region recorded in the distribution map information.

In another aspect of the above update device, after the generation of the information of the region that satisfies the predetermined condition, the update device is configured to perform measurement point group information of the region different from the first point group information used for generating the information. A verification unit that verifies the reliability of the generated information based on the collation result is further provided. According to this aspect, the update device can verify the reliability of the information in the generated region and reflect only the reliable information in the distribution map information.

According to another preferred embodiment of the present invention, there is provided a control method executed by the updating device, comprising map point cloud information given for each of a plurality of areas of map information and a difference greater than or equal to a predetermined value, and measuring An acquisition step of acquiring measurement point group information about each measurement distance from a reference position to a plurality of positions measured by a unit from a plurality of moving bodies, and the measurement point group information for each of the plurality of moving bodies, and An update step of updating information on a region satisfying a predetermined condition among the plurality of regions based on a collation result of the map point cloud information. By executing this control method, the update device can suitably update the information for the region where it is estimated that the static structure has changed.

According to another preferred embodiment of the present invention, there is provided a program executed by a computer, the map point cloud information assigned to each of a plurality of areas of map information and a difference greater than or equal to a predetermined value, An acquisition unit that acquires measurement point group information about each measurement distance from a reference position to a plurality of positions from a plurality of moving bodies, and the measurement point group information and map points for each of the plurality of moving bodies Based on the group information collation result, the computer is caused to function as an update unit that updates information of a region satisfying a predetermined condition among the plurality of regions. By executing this program, the computer can preferably update the information on the region where the static structure is estimated to have changed. Preferably, the program is stored in a storage medium.

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

[Outline of driving support system]
FIG. 1 is a schematic configuration of a driving support system according to the present embodiment. The driving support system includes an in-vehicle device 1 that moves together with a vehicle, and a server device 2 that distributes map information. In FIG. 1, only one set of the in-vehicle device 1 and the vehicle that communicates with the server device 2 is displayed, but actually there are a plurality of sets of the in-vehicle device 1 and the vehicle at different positions.

The in-vehicle device 1 is electrically connected to an external sensor such as a lidar (Lidal: Light Detection and Ranging, or Laser Illuminated Detection And Ranging), an internal sensor such as a gyro sensor or a vehicle speed sensor, and based on these outputs. The position of the vehicle on which the in-vehicle device 1 is mounted (also referred to as “own vehicle position”) is estimated. And the vehicle equipment 1 performs automatic driving | operation control etc. of a vehicle so that it drive | works along the path | route to the set destination based on the estimation result of the own vehicle position. The in-vehicle device 1 stores a map database (DB: DataBase) 10 including voxel data. The voxel data is data in which position information of a stationary structure is recorded for each area (also referred to as “voxel”) when the three-dimensional space is divided into a plurality of areas. The voxel data includes data representing point cloud data measured for stationary structures in each voxel by a normal distribution, and is used for scan matching using NDT (Normal Distributions Transform) as will be described later.

The in-vehicle device 1 performs scan matching based on NDT based on the point cloud data output by the lidar and the voxel data corresponding to the voxel to which the point cloud data belongs. And the vehicle equipment 1 transmits the information regarding the said voxel (it is also called "matching fall information D1") to the server apparatus 2, when the voxel with low matching precision is detected. In addition, when the vehicle-mounted device 1 receives information specifying a specific voxel (also referred to as “request signal D2”) from the server device 2, measurement data (“measurement data D3” by a lidar or the like in the specified voxel is received. Is also transmitted to the server device 2.

The server device 2 performs data communication with the in-vehicle device 1 corresponding to a plurality of vehicles. The server device 2 stores a distribution map DB 20 for distribution to the vehicle-mounted device 1 corresponding to a plurality of vehicles, and the distribution map DB 20 includes voxel data corresponding to each voxel. The server device 2 accumulates the matching decrease information D1 received from the in-vehicle device 1, and determines whether or not the voxel data needs to be updated for a specific voxel based on the accumulated matching decrease information D1. And when the server apparatus 2 judges that the update of the voxel data with respect to a specific voxel is required, it transmits the request signal D2 which designated the said voxel to each vehicle equipment 1. And the server apparatus 2 receives the measurement data D3 from the vehicle equipment 1 as a response of the request signal D2. And the server apparatus 2 performs the process required for the update of the target voxel data based on the matching fall information D1 and the measurement data D3. The server device 2 is an example of an “update device” in the present invention, and the distribution map DB 20 is an example of “distribution map information” in the present invention.

[Configuration of in-vehicle device]
FIG. 2A shows a block diagram illustrating a functional configuration of the vehicle-mounted device 1. As shown in FIG. 2A, the in-vehicle device 1 mainly includes a communication unit 11, a storage unit 12, a sensor unit 13, an input unit 14, a control unit 15, and an output unit 16. The communication unit 11, the storage unit 12, the sensor unit 13, the input unit 14, the control unit 15, and the output unit 16 are connected to each other via a bus line.

The communication unit 11 receives the map information distributed from the server device 2 based on the control of the control unit 15 or transmits the matching degradation information D1 generated by the control unit 15 to the server device 2. Moreover, the communication part 11 transmits the measurement data D3 to the server apparatus 2 based on control of the control part 15, when the request signal D2 is received. Moreover, the communication part 11 transmits the signal for controlling a vehicle to a vehicle, or receives the signal regarding the state of a vehicle from a vehicle.

The storage unit 12 stores a program executed by the control unit 15 and information necessary for the control unit 15 to execute a predetermined process. In the present embodiment, the storage unit 12 stores a map DB 10 including voxel data.

The sensor unit 13 includes a rider 30, a camera 31, a GPS receiver 32, a gyro sensor 33, and a speed sensor 34. The lidar 30 emits a pulse laser in a predetermined angular range in the horizontal direction and the vertical direction, thereby discretely measuring the distance to an object existing in the outside world, and a three-dimensional point indicating the position of the object Generate group data. In this case, the lidar 30 scans data based on an irradiation unit that emits laser light while changing the irradiation direction, a light receiving unit that receives reflected light (scattered light) of the irradiated laser light, and a light reception signal output by the light receiving unit. Output unit. The scan data is generated based on the irradiation direction corresponding to the laser beam received by the light receiving unit and the response delay time of the laser beam specified based on the above-described received light signal. The lidar 30 is an example of the “measurement unit” in the present invention, and the point cloud data output from the lidar 30 is an example of the “measurement point cloud information” in the present invention.

The input unit 14 is a button, a touch panel, a remote controller, a voice input device, or the like for a user to operate, and accepts an input for specifying a destination for route search, an input for specifying on / off of automatic driving, and the like. The generated input signal is supplied to the control unit 15. The output unit 16 is, for example, a display or a speaker that performs output based on the control of the control unit 15.

The control unit 15 includes a CPU that executes a program and controls the entire vehicle-mounted device 1. For example, the control unit 15 estimates the host vehicle position by performing scan matching based on NDT based on the point cloud data output from the lidar 30 and the voxel data corresponding to the voxel to which the point cloud data belongs. Do. Further, the control unit 15 detects a voxel that is estimated to have low matching accuracy based on an evaluation value for each voxel obtained by scan matching based on NDT. And the control part 15 produces | generates the matching fall information D1 corresponding to the detected voxel, and transmits the matching fall information D1 to the server apparatus 2 by the communication part 11. FIG. Further, when the control unit 15 determines that the voxel specified by the request signal D2 received from the server device 2 belongs to the measurement range of the rider 30, the point cloud data belonging to the voxel from the point cloud data output by the rider 30. Is extracted and transmitted to the server device 2 as measurement data D3.

[Configuration of server device]
FIG. 2B shows a schematic configuration of the server device 2. As illustrated in FIG. 2B, the server device 2 includes a communication unit 21, a storage unit 22, and a control unit 25. The communication unit 21, the storage unit 22, and the control unit 25 are connected to each other via a bus line.

The communication unit 21 communicates various data with the in-vehicle device 1 based on the control of the control unit 25. The storage unit 22 stores a program for controlling the operation of the server device 2 and holds information necessary for the operation of the server device 2. In addition, the storage unit 22 stores the distribution map DB 20 and also stores the matching decrease information D1 and the measurement data D3 transmitted from the plurality of in-vehicle devices 1.

The control unit 25 includes a CPU, a ROM, a RAM, and the like (not shown), and performs various controls on each component in the server device 2. In the present embodiment, the control unit 25 accumulates the matching decrease information D1 received from the in-vehicle device 1 in the storage unit 22, and determines whether or not it is necessary to update voxel data for a specific voxel based on the accumulated matching decrease information D1. . And when the control part 25 judges that the update of the voxel data with respect to a specific voxel is required, the request signal D2 which designated the said voxel is transmitted to each vehicle equipment 1 by the communication part 21. FIG. Then, the control unit 25 updates the voxel data included in the distribution map DB 20 based on the point cloud data included in the matching degradation information D1 and the point cloud data indicated by the measurement data D3 received from the in-vehicle device 1 based on the request signal D2. Perform processing. The control unit 25 is an example of an “acquisition unit”, “update unit”, “verification unit”, and “computer” that executes a program in the present invention.

[Scan matching based on NDT]
Next, scan matching based on NDT in the present embodiment will be described.

(1) Data structure of voxel data First, voxel data used for scan matching based on NDT will be described. FIG. 3 shows an example of a schematic data structure of voxel data.

The voxel data includes parameter information when the point group in the voxel is expressed by a normal distribution. In this embodiment, as shown in FIG. 3, the voxel ID, voxel coordinates, average vector, and covariance matrix are used. And a weight value and point cloud number information. Here, “voxel coordinates” indicate absolute three-dimensional coordinates of a reference position such as the center position of each voxel. Each voxel is a cube obtained by dividing the space into a lattice shape, and since the shape and size are determined in advance, the space of each voxel can be specified by the voxel coordinates. The voxel coordinates may be used as a voxel ID.

“Average vector” and “covariance matrix” indicate an average vector and a covariance matrix corresponding to parameters when a point group in the target voxel is expressed by a normal distribution, and an arbitrary vector in any voxel “k” The coordinates of the point "i"

Is defined as, when a point number set in the voxel k to "N _k", mean vector "mu _k" and the covariance matrix "V _k" at the voxel k, respectively the following formulas (1) and ( 2).

The average vector and covariance matrix included in the voxel data are an example of “map point cloud information” in the present invention.

The “weighting value” is set to a value corresponding to the reliability of the voxel data (particularly the average vector and covariance matrix) of the target voxel, and represents a weighting value for the target voxel set in the scan matching. The weighting value is an example of “reliability information” in the present invention. “Point cloud number information” is information indicating the number of point clouds used for calculating the corresponding average vector and covariance matrix. The point group number information may be information indicating the number of specific point groups, or information indicating the level of the number of point groups (for example, large, medium, small, etc.).

(2) Outline of Scan Matching Next, scan matching by NDT using voxel data will be described. In the present embodiment, as will be described later, the vehicle-mounted device 1 normalizes the value (evaluation value) of the evaluation function obtained by NDT scan matching based on the number of point groups measured in the voxel and is included in the voxel data. The weighted value is used for calculation. As a result, the in-vehicle device 1 accurately specifies voxels with relatively low scan matching accuracy based on the evaluation value, and suitably improves the position estimation accuracy based on NDT scan matching.

Scan matching by NDT assuming a vehicle is to estimate the following estimated parameter “P” with the amount of movement in the road plane (here, xy coordinates) and the direction of the vehicle as elements.

“T _x ” indicates the amount of movement in the x direction, “t _y ” indicates the amount of movement in the y direction, and “Ψ” indicates the rotation angle (ie, yaw angle) in the xy plane. The vertical movement amount, pitch angle, and roll angle are small enough to be ignored, although they are caused by road gradients and vibrations.

When the coordinates [x _k (i), y _k (i), z _k (i)] ^T of the arbitrary point of the point group data obtained by the lidar 30 are subjected to coordinate conversion using the above-described estimation parameter P, the converted value The coordinates “X ′ _k (i)” are expressed by the following equation (3).

In this embodiment, the in-vehicle device 1 uses the coordinate-converted point group, the average vector μ _k and the covariance matrix V _k included in the voxel data, and the voxel _{k represented} by the following equation (4). A comprehensive evaluation function “E” (also referred to as “overall evaluation function”) for all voxels to be matched indicated by the evaluation function “E _k ” and Expression (5) is calculated.

“M” indicates the number of voxels to be matched, and “w _k ” indicates a weighting value for voxel k. According to Expression (4), the evaluation function E _k becomes a larger value as the weighting value w _k is larger. In addition, since normalization is performed using the number of point groups N _k , differences due to the number of point groups are reduced. The coordinates of the point cloud data obtained by the lidar 30 are relative coordinates with respect to the vehicle position, and the average vector of the voxel data is an absolute coordinate. Therefore, when calculating the equation (4), for example, the lidar The coordinates of the point cloud data obtained by 30 are converted based on the vehicle position predicted from the output of the GPS receiver 32 or the like.

On the other hand, the evaluation function E _k of the voxel k used in the conventional NDT matching is expressed by the following equation (6).

As is clear from the comparison between the equations (4) and (6), in the present embodiment, the in-vehicle device 1 normalizes the evaluation function E _k by the number of point groups N _k . Thus, the vehicle-mounted device 1, as described later, evaluated based on the value of the function E _k, it is the degree of matching to accurately identify the relatively low voxel. The in-vehicle device 1 multiplies each voxel by a weighting value corresponding to the reliability of each voxel data (average vector, covariance matrix). As a result, the in-vehicle device 1 relatively reduces the weighting of the evaluation function E _k of the voxel with low reliability, and suitably improves the position estimation accuracy by NDT matching.

Thereafter, the vehicle-mounted device 1 calculates an estimation parameter P that maximizes the comprehensive evaluation function E by an arbitrary root finding algorithm such as Newton's method. The in-vehicle device 1 estimates the own vehicle position with high accuracy by applying the estimation parameter P to the own vehicle position predicted from the output of the GPS receiver 32 or the like.

(3) Specific Example of Scan Matching Next, a specific example of NDT scan matching will be described. Hereinafter, for convenience of explanation, the case of a two-dimensional plane will be described as an example.

FIG. 4 (A) shows, in circles, point groups measured by a rider or the like when traveling with a measurement maintenance vehicle for map creation in four adjacent voxels “B1” to “B4”. It is the figure which showed the two-dimensional normal distribution created from Formula (1) and Formula (2) based on this by gradation. The average and variance of the normal distribution shown in FIG. 4A correspond to the average vector and covariance matrix in the voxel data, respectively.

FIG. 4B is a diagram showing the point cloud acquired by the lidar 30 while the vehicle-mounted device 1 is traveling in FIG. The position of the point cloud of the lidar 30 indicated by the asterisk is aligned with the voxels B1 to B4 based on the estimated position based on the output of the GPS receiver 32 or the like. In the example of FIG. 4B, there is a deviation between the point group (circle) measured by the measurement and maintenance vehicle and the point group (star) acquired by the in-vehicle device 1.

FIG. 4C is a diagram illustrating a state after the point cloud (star) acquired by the vehicle-mounted device 1 is moved based on the matching result of the NDT scan matching. In FIG. 4C, a parameter P that maximizes the evaluation function E shown in the equations (4) and (5) is calculated based on the mean and variance of the normal distribution shown in FIGS. 4 (A) and 4 (B). The calculated parameter P is applied to the star point cloud shown in FIG. In this case, the deviation between the point cloud (circle) measured by the measurement and maintenance vehicle and the point cloud (star) acquired by the in-vehicle device 1 is suitably reduced.

Here, when the evaluation functions “E1” to “E4” and the comprehensive evaluation function E corresponding to the voxels B1 to B4 are calculated by the general formula (6) conventionally used, these values are as follows: Become.

E1 = 1.3290
E2 = 1.1365
E3 = 1.1100
E4 = 0.9686
E = 4.5441
In this case, since the value of the evaluation function increases as the number of point groups in the voxel increases, it is difficult to compare the degree of matching between voxels. In this example, there is no significant difference in the evaluation functions E1 to E4 of each voxel, but there are some differences depending on the number of point groups included in the voxel.

On the other hand, when the evaluation functions E1 to E4 and the comprehensive evaluation function E corresponding to the voxels B1 to B4 are calculated by the equation (4) based on the present embodiment, these values are as follows. Here, it is assumed that the weighting values for voxels B1 to B4 are all equal.

E1 = 0.1208
E2 = 0.136
E3 = 0.1233
E4 = 0.1211
E = 0.4789
In this case, the evaluation functions E1 to E4 and the comprehensive evaluation function E are values that are not easily affected by the number of point groups in the voxel, so that the degree of matching between the voxels can be easily compared.

In this embodiment, a weight value is set for each voxel. Therefore, it is possible to increase the degree of matching of voxels by increasing the weighting of voxels with high reliability.

FIG. 5A is a diagram showing a matching result when the weighting values for voxels B1 to B4 are all equal (that is, the same diagram as FIG. 4C). FIG. 5B is a diagram illustrating a matching result when the weighting value of the voxel B1 is 10 times the weighting value of the other voxels. FIG. 5C is a diagram showing a matching result when the weighting value of the voxel B3 is 10 times the weighting value of the other voxels.

In the example of FIG. 5B, the values of the evaluation functions E1 to E4 and the comprehensive evaluation function E corresponding to the voxels B1 to B4 are as follows.

E1 = 0.3720
E2 = 0.0350
E3 = 0.0379
E4 = 0.0373
E = 0.4823
Thus, in the example of FIG. 5B, matching is performed so that the value of the evaluation function E1 corresponding to the voxel B1 is high, and the degree of matching in the voxel B1 is increased. Therefore, the deviation between the circle and star of voxel B1 is reduced. Moreover, since the value of the evaluation function is small because it is normalized by the number of point groups, each evaluation function value has a ratio similar to the weighting value.

In the example of FIG. 5C, the values of the evaluation functions E1 to E4 and the comprehensive evaluation function E corresponding to the voxels B1 to B4 are as follows.

E1 = 0.0368
E2 = 0.0341
E3 = 0.3822
E4 = 0.0365
E = 0.4896
As described above, in the example of FIG. 5C, matching is performed such that the value of the evaluation function E3 corresponding to the voxel B3 is high, and the degree of matching in the voxel B3 is increased. Therefore, the deviation between the circle mark and the star mark of the voxel B3 is reduced.

[Map update processing]
Next, processing related to the update processing of the distribution map DB 20 will be described.

(1) Transmission processing of matching deterioration information In the in- vehicle device 1, there is a possibility that a static structure has changed (that is, the distribution map DB 20 needs to be updated) based on the comprehensive evaluation function E and the evaluation function E _k. The voxel is specified, and information related to the voxel is transmitted to the server apparatus 2 as matching reduction information D1.

In general, when the overall evaluation function E or the evaluation function E _k becomes low (that is, when the matching degree becomes low), the following three events:
(A) A dynamic object is included in the voxel. (B) There are many errors in the predicted vehicle position and accurate matching is not possible. (C) A static object in the voxel is changed (generated, It is presumed that one of them has occurred). Here, in this embodiment, since the evaluation function E _k is normalized by the point group number N _k , each evaluation function E _k and the total evaluation function E that is the sum thereof are affected by the point group number N _k . Not receive. Therefore, when the comprehensive evaluation function E is larger than the predetermined value, it can be determined that the event (b) has not occurred.

On the other hand, among the evaluation function E _k, if the smallest evaluation function E _k as compared with other evaluation functions E _k exists, the voxel k corresponding to the evaluation function E _k, event (a) or event (c) Is suspected of occurring. Therefore, when the in-vehicle device 1 detects such a voxel k, the in-vehicle device 1 transmits matching lowering information D1 related to the voxel k to the server device 2. Here, the matching reduction information D1 includes, for example, the point cloud data of the rider 30 used for calculation of the evaluation function E _k , time information, estimated vehicle position information, comprehensive evaluation function E, evaluation function E _k , voxel ID, And the number N _{k of} point groups. Thereafter, the server device 2 determines whether the event (a) or the event (c) is applied to the voxel k by a statistical method based on the plurality of matching decrease information D1 regarding the target voxel k received from the vehicle-mounted devices 1 of the plurality of vehicles. Determine if it has occurred.

Here, an example of a method for detecting an evaluation function E _k that is smaller than other evaluation functions E _k out of the evaluation functions E _k will be described. When the value obtained by dividing the evaluation function E _k by a reference value “F _k ”, which will be described later, is smaller than the predetermined value “A”, the in-vehicle device 1, that is, the conditional expression E _k / F _k <A Formula (7)
Is satisfied, it is determined that the evaluation function E _k is relatively smaller than the other evaluation functions E _k, and matching reduction information D1 for the voxel of the evaluation function E _k is transmitted to the server device 2. Expression (7) is an expression equivalent to the following expression (8).

E _k <A · F _k formula (8)
Here, the vehicle-mounted device 1 calculates the reference value F _k in the above-described determination formula based on the following formula (9).

As shown in Expression (9), the reference value F _k corresponds to the comprehensive evaluation function E that is weighted based on the weight value w _k of the target voxel k. By doing so, the vehicle-mounted device 1 can be suitably detect a relatively small evaluation function E _k as compared with other evaluation functions E _k.

In addition, in-vehicle device 1 detects a voxel with a small number of point groups N _k in the voxel in addition to or instead of detecting an evaluation function E _k that is smaller than other evaluation functions E _k . In this case, the matching reduction information D1 for the voxel may be transmitted to the server device 2. In this embodiment, evaluation for function E _k is normalized by point group number N _k, even if the event (a) or event number point group due to (c) N _k is small, evaluation there is a case where the value of the function E _k is not smaller than the value of the other evaluation functions E _k. Therefore, the vehicle-mounted device 1 determines that there is a high possibility that either the event (a) or the event (c) has occurred when a voxel having a point cloud number _Nk smaller than a predetermined threshold is detected. The matching reduction information D1 for the voxel is transmitted to the server device 2.

In this case, preferably, the in-vehicle device 1 refers to the point cloud number information of the voxel data and sets the above-described threshold according to the point cloud number information. In this case, the in-vehicle device 1 sets the above threshold value smaller as the point cloud number indicated by the point cloud number information is smaller. Thereby, the vehicle equipment 1 can determine suitably by the above-mentioned threshold value whether the number of point groups _Nk is smaller than the number of point groups which should be acquired originally. The in-vehicle device 1 is a case where the voxel k having a smaller evaluation function E _k than the other evaluation functions E _k is detected based on the equation (7) and the number of point groups in the voxel k. When N _k is smaller than the threshold value, the matching decrease information D1 for the voxel k may be transmitted to the server device 2.

(2) The weight change processing server device 2 accumulates the matching decrease information D1 received from the vehicle-mounted devices 1 of a plurality of vehicles for each voxel ID, and either the event (a) or the event (c) described above for each voxel ID. It is statistically determined whether or not the above has occurred.

For example, when there is a voxel in which the number of matching degradation information D1 for each voxel ID is accumulated more than a predetermined number, the server apparatus 2 has a high possibility that the event (c) has occurred and corresponds to the voxel. The voxel data to be determined is low in reliability. Therefore, the server apparatus 2 decreases the weight value included in the voxel data corresponding to the voxel. At this time, preferably, the server device 2 may set the weighting value to be smaller as the number of matching deterioration information D1 is larger. By doing in this way, the server apparatus 2 reduces the weighting of the voxel that is likely to have the event (c), and performs the vehicle position estimation based on the map information distributed from the server apparatus 2 The position estimation accuracy at 1 is preferably improved.

Here, a specific example of changing the weighting value will be described with reference to FIG.

FIG. 6A is a diagram showing a point cloud (star symbol) acquired by the vehicle-mounted device 1 when the stationary structure at the position of the voxel B3 changes. In addition, the point cloud (circle mark) which the measurement maintenance vehicle acquired at the time of producing voxel data before a stationary structure changes is also shown. As shown in FIG. 6A, when the stationary structure at the position of the voxel B3 changes, the number of point groups acquired by the rider 30 in the voxel 3 decreases, or the points acquired by the measurement and maintenance vehicle A point group out of the group is acquired by the lidar 30. The same applies when a dynamic object enters a position in the voxel B3.

FIG. 6B is a diagram showing a matching result when the weighting values for the voxels B1 to B4 are all equal. Due to the influence of the deviation of the voxel B3, the deviation between the circle mark and the star mark of the voxels B1, B2, B4 is also large. Therefore, the matching is shifted, and an error occurs in the position estimation result. In this case, the values of the evaluation functions E1 to E4 and the comprehensive evaluation function E corresponding to the voxels B1 to B4 are as follows.

E1 = 0.1188
E2 = 0.150
E3 = 0.0568
E4 = 0.1120
E = 0.4026
In this case, the value of the evaluation function E3 of the voxel B3 is smaller than the others. When equation (9) is calculated,

F1 = F2 = F3 = F4≈0.1007
Therefore, the left side of Equation (7) is as follows.

E1 / F1≈1.1803
E2 / F2≈1.1426
E3 / F3≈0.5643
E4 / F4≈1.1128
Therefore, for example, when the predetermined value A is 0.7, E3 / F3 satisfies the conditional expression (7). Therefore, in this case, the in-vehicle device 1 determines that the evaluation function E3 is relatively smaller than the other evaluation functions, and transmits the matching reduction information D1 related to the voxel B3 to the server device 2.

FIG. 6C shows the matching result after changing the weighting value of voxel B3. In the example of FIG. 6C, the server device 2 sets the weighting value of the voxel B3 to 1/10 based on the matching reduction information D1 of the voxel B3 transmitted from the vehicle-mounted devices 1 of a plurality of vehicles. Thereby, the influence of the deviation of the voxel B3 is weakened, and the deviation between the circle mark and the star mark of the voxels B1, B2, B4 is reduced. In this case, the values of the evaluation functions E1 to E4 and the comprehensive evaluation function E corresponding to the voxels B1 to B4 are as follows.

E1 = 0.1559
E2 = 0.1474
E3 = 0.039
E4 = 0.1557
E = 0.4629
Thus, when the weighting value for voxel B3 is set to 1/10, matching is performed so as to increase the degree of matching for voxels other than voxel B3. As a result, it is possible to estimate the position with a low influence degree of the voxel B3 having low reliability. In this case, if equation (9) is calculated,

F1 = F2 = F4≈0.1493, F3≈0.0149
Therefore, the left side of Equation (7) is as follows.

E1 / F1≈1.0442
E2 / F2≈0.9873
E3 / F3≈0.2617
E4 / F4≈1.0429
Therefore, in order to satisfy the condition of Expression (7), the matching degradation information D1 related to the voxel B3 is transmitted to the server device 2 in this case as well.

In this way, by normalizing the evaluation function E _k with the reference value F _k , it is possible to determine a condition that is not affected by the weighting value.

(3) The voxel data correction processing server device 2 determines whether or not it is necessary to correct voxel data such as an average vector and a covariance matrix related to the voxel with respect to the voxel whose weight value is lowered by the above-described weight change processing.

For example, the server device 2 determines whether or not the evaluation function E _k included in the matching decrease information D1 of the target voxel is similar. When the server device 2 determines that the evaluation functions E _k included in the matching degradation information D1 are similar, it can be assumed that the stationary structure has changed, so that the correction of the voxel data for the target voxel is performed. Is determined to be necessary. In this case, the server device 2 calculates the index value in accordance with the above-described presence / absence of the similarity or the variance of the evaluation function E _k included in the matching reduction information D1, and compares the calculated value with a predetermined threshold value. You may determine by doing. When there is a voxel that is determined to require correction of the voxel data (also referred to as “target voxel Btag”), the server device 2 transmits a request signal D2 designating the target voxel Btag to each vehicle-mounted device 1. .

Next, the server device 2 corrects the voxel data for the target voxel Btag based on the measurement data of the target voxel Btag included in the matching degradation information D1 and the measurement data D3 received as a response to the request signal D2. In this case, the server device 2 preferably uses the NDT data (that is, the average vector, covariance matrix, point cloud number information, etc.) of the target voxel Btag based on the measurement data for which the comprehensive evaluation function E is equal to or greater than a predetermined value. Generate. Generally, when the comprehensive evaluation function E is high, the vehicle position can be estimated with high accuracy, and it is estimated that the measurement data used for calculating the comprehensive evaluation function E is also highly reliable. Considering the above, the server device 2 determines whether or not the measurement data is to be used for updating the voxel data based on the comprehensive evaluation function E.

Furthermore, the server device 2 performs verification on the reliability (validity) of the generated NDT data. For example, the server device 2 performs matching between the generated NDT data and measurement data D3 not used for generating the NDT data, and performs the above-described verification based on the evaluation value obtained by the matching. In this case, the server device 2 may acquire the plurality of evaluation values by performing the above-described matching on the plurality of measurement data D3, and perform the above-described verification based on the plurality of acquired evaluation values. For example, in the server device 2, when a predetermined ratio (for example, 90%) or more of the plurality of evaluation values is equal to or greater than the predetermined value, the generated NDT data is highly reliable and updatable data. Judge.

When the server device 2 determines that the generated NDT data is reliable as a result of the above verification, the server device 2 updates the voxel data of the target voxel Btag of the distribution map DB 20 with the generated NDT data. Further, the server device 2 sets the weighting value of the voxel data of the target voxel Btag to an initial value (for example, 1). Then, the server device 2 distributes the updated map data including the updated voxel data to the vehicle-mounted device 1 of each vehicle at a predetermined timing, thereby converting the voxel data in the map DB 10 into the voxel data in the distribution map DB 20. Synchronize with. By doing in this way, the server apparatus 2 can improve the position estimation precision around the object voxel Btag which the vehicle equipment 1 performs suitably.

[Processing flow]
(1) In- vehicle device processing FIG. 7 is an example of a flowchart showing a processing procedure executed by the in-vehicle device 1 in this embodiment. The in-vehicle device 1 repeatedly executes the process of the flowchart of FIG.

First, the in-vehicle device 1 sets an initial value of the vehicle position based on the output of the GPS receiver 32 or the like (step S101). Next, the vehicle-mounted device 1 acquires the vehicle body speed from the speed sensor 34 and also acquires the angular velocity in the yaw direction from the gyro sensor 33 (step S102). And the vehicle equipment 1 calculates the moving distance of a vehicle and the azimuth | direction change of a vehicle based on the acquisition result of step S102 (step S103).

Thereafter, the vehicle-mounted device 1 adds the movement distance and the azimuth change calculated in step S103 to the estimated host vehicle position one time before, and calculates a predicted position (step S104). And the vehicle equipment 1 acquires the voxel data of the voxel which exists around the own vehicle position with reference to map DB10 based on the estimated position calculated by step S104 (step S105). Further, the in-vehicle device 1 divides the scan data obtained from the lidar 30 for each voxel based on the predicted position calculated in step S104 (step S106). And the vehicle equipment 1 calculates NDT scan matching using an evaluation function (step S107). In this case, the in-vehicle device 1 calculates the evaluation function E _k and the comprehensive evaluation function E based on the equations (4) and (5), and calculates the estimation parameter P that maximizes the comprehensive evaluation function E.

The vehicle unit 1, when the synthetic evaluation function E has identified the estimated parameter P becomes maximum (step S108; Yes), by using the synthetic evaluation function E and the weighting values w _k of each voxel, formula (9) The reference value _Fk is calculated based on (Step S109). Then, the in-vehicle device 1 compares the reference value F _k with the evaluation function E _k for each voxel (step S110), and determines whether there is a voxel whose comparison result is smaller than the predetermined value A (step S111). ). That is, the in-vehicle device 1 determines whether or not there is an evaluation function E _k that satisfies the equation (7).

And when the voxel whose comparison result is smaller than the predetermined value A exists (step S111; Yes), the vehicle-mounted device 1 measures the point cloud data, the time, the estimated position, the comprehensive evaluation function E, and the voxel ID of the target voxel. Then, the matching reduction information D1 including the evaluation function E _k and the point cloud number N _k is transmitted to the server device 2 (step S112). Note that the in-vehicle device 1 is based on the magnitude of the point cloud number N _k as described in the section “(1) Matching degradation information transmission process ” instead of or in addition to the determination in step S111. It may be determined whether or not the matching degradation information D1 needs to be transmitted.

On the other hand, when there is no voxel whose comparison result is smaller than the predetermined value A (step S111; No), the in-vehicle device 1 returns the process to step S102. The in-vehicle device 1 calculates the estimated own vehicle position at the current time by applying the estimated parameter P that maximizes the comprehensive evaluation function E to the predicted position in step S104 after the determination in step S111.

Next, the in-vehicle device 1 determines whether or not the request signal D2 designating the voxel around the vehicle position is received from the server device 2 in step S113 (step S113). And the vehicle equipment 1 will receive the request signal D2 which designated the voxel around the own vehicle position from the server apparatus 2 (step S113; Yes), and the scan data of the lidar 30 corresponding to the voxel designated by the request signal D2 ( Point cloud data) is transmitted to the server apparatus 2 as measurement data D3 (step S114). At this time, the in-vehicle device 1 may include the comprehensive evaluation function E at the time when the scan data is acquired in the measurement data D3 in addition to the scan data. The comprehensive evaluation function E included in the measurement data D3 is used in the processing of the server device 2 described later. And the vehicle equipment 1 returns a process to step S102, after performing step S114, or when it is judged by step S113 that the request signal D2 which designated the voxel around the own vehicle position is not received.

(2) Processing of Server Device FIG. 8 is an example of a flowchart showing a processing procedure executed by the server device 2 in the present embodiment. The server device 2 repeatedly executes the process of the flowchart of FIG.

First, the server device 2 receives the matching decrease information D1 from the in-vehicle device 1 mounted on the vehicle (step S201). And the server apparatus 2 memorize | stores the matching fall information D1 in the memory | storage part 22 for every voxel ID.

Next, the server device 2 refers to the storage unit 22 and determines whether there is a voxel in which the number of matching degradation information D1 is greater than a predetermined value (step S202). Then, when there is a voxel in which the number of matching decrease information D1 is larger than a predetermined value (step S202; Yes), the server device 2 stores the corresponding voxel distribution map DB 20 as the number of matching decrease information D1 increases. The weighted value w _k thus set is set small (step S203). Thereby, the server device 2 relatively reduces the matching degree before updating the voxel data for the voxel whose static structure has changed, and preferably reduces the position estimation accuracy before the voxel data is updated. Can be suppressed. Note that the initial value of the weighting value w _k stored in the distribution map DB 20 is set to an initial value (for example, 1) common to the voxels, for example.

Next, the server device 2 determines whether or not to correct the voxel data of the voxel whose weight value w _k is reduced (step S204). In this case, for example, the server device 2 performs the above-described determination based on whether or not the evaluation function E _k included in the plurality of matching deterioration information D1 of the target voxel is similar.

When the server device 2 determines that the voxel data should be corrected (step S204; Yes), the server device 2 transmits a request signal D2 for requesting scan data of the target voxel Btag to the vehicle-mounted device 1 of each vehicle (step S204). S205). And the server apparatus 2 receives the measurement data D3 containing the scan data of the object voxel Btag from the vehicle equipment 1 of each vehicle as a response of the request signal D2, and memorize | stores it in the memory | storage part 22 (step S206). The measurement data D3 includes, in addition to the scan data, a comprehensive evaluation function E at the time when the scan data is acquired. On the other hand, when the server apparatus 2 determines that the voxel data need not be corrected (step S204; No), the process returns to step S201.

Next, for the target voxel Btag for which the voxel data is determined to be corrected in step S204, the server device 2 has measured data with a high overall evaluation function E (both measurement data and measurement data D3 included in the matching reduction information D1). It is determined whether or not (including) has been accumulated (step S207). Specifically, the server device 2 determines whether or not a predetermined number or more of measurement data whose comprehensive evaluation function E is higher than a predetermined threshold is accumulated. Thereby, the server device 2 determines whether or not measurement data necessary for updating the voxel data has been collected.

Then, when measurement data having a high comprehensive evaluation function E is accumulated (step S207; Yes), the server device 2 constructs point cloud data of the target voxel by weighted averaging based on the value of the comprehensive evaluation function E. (Step S208). Thereby, when constructing the point cloud data of the target voxel, the server device 2 constructs highly accurate point cloud data by increasing the weighting of scan data with higher reliability. On the other hand, when the measurement data D3 having a high comprehensive evaluation function E is not accumulated (step S207; No), the server apparatus 2 returns the process to step S201. In this case, the server device 2 outputs a predetermined warning to the administrator, and informs that there is a voxel whose voxel data should be corrected and that the measurement maintenance vehicle travels on a road where the voxel falls within the measurement range. The administrator may be notified that the point cloud data needs to be measured.

Next, the server device 2 generates NDT data (that is, average vector, covariance matrix, point group number information, etc.) from the point cloud data constructed in step S208 (step S209). Then, the server device 2 verifies the generated NDT data using other measurement data D3 acquired from the vehicle (that is, measurement data D3 not used for generating NDT data) (step S210). In this case, for example, the server device 2 performs matching between the generated NDT data and verification measurement data D3, and performs the above-described verification based on the evaluation value obtained by the matching.

When the server device 2 determines that the generated NDT data is reliable as a result of the verification in step S210 (step S211; Yes), the target voxel data in the distribution map DB 20 is based on the processing result in step S209. Is updated (step S212). And the server apparatus 2 sets the weighting value of the voxel which updated the voxel data to an initial value (step S212). On the other hand, when the server device 2 determines that the reliability of the generated NDT data is low as a result of the verification in step S210 (step S211; No), the server device 2 does not update the voxel data in the distribution map DB 20, and performs step S201. Return processing to. In this case, the server device 2 outputs a predetermined warning to the administrator, and informs that there is a voxel whose voxel data should be corrected and that the measurement maintenance vehicle travels on a road where the voxel falls within the measurement range. The administrator may be notified that the point cloud data needs to be measured.

As described above, the server device 2 according to the present embodiment matches the point cloud data measured by the rider 30 with the point cloud data indicated by the voxel data included in the map DB 10 from the in-vehicle devices 1 of a plurality of vehicles. The matching reduction information D1 regarding the voxel with a low degree is received. And the server apparatus 2 determines the object voxel Btag used as update object based on the matching fall information D1, and updates the voxel data of the object voxel Btag based on measurement data with high comprehensive evaluation function E. FIG. Thereby, the server apparatus 2 can perform the update process of voxel data exactly.

[Modification]
Hereinafter, modified examples suitable for the embodiments will be described. The following modifications may be applied to the embodiments in combination.

(Modification 1)
The voxel data is not limited to a data structure including an average vector and a covariance matrix as shown in FIG. For example, the voxel data may include point cloud data measured by a measurement and maintenance vehicle used when calculating an average vector and a covariance matrix. In this case, the point cloud data included in the voxel data is an example of “map point cloud information” in the present invention.

Further, the present embodiment is not limited to scan matching by NDT, and other scan matching such as ICP (Iterative Closest Point) may be applied. Even in this case, as in the embodiment, the in-vehicle device 1 identifies a voxel having a relatively low matching degree based on the evaluation value for each voxel for which the degree of matching is evaluated, and the matching reduction information D1 for the voxel. Is transmitted to the server device 2. And the server apparatus 2 performs update of a weighting value, transmission of the request signal D2, reception of the measurement data D3, etc. similarly to the flowchart of FIG. Then, the server device 2 generates voxel data (in this case, point cloud data) of the target voxel Btag based on measurement data having a high evaluation value, and updates the voxel data in the distribution map DB 20. Thus, the scan matching method applicable to the present invention is not limited to NDT scan matching.

(Modification 2)
With respect to the verification processing in step S210 of FIG. 8, the server device 2 performs the matching between the generated NDT data and the measurement data D3, instead of performing the matching between the generated point data of the target voxel Btag and the measurement data D3. Matching with the point cloud data may be performed. In this case, for example, the server device 2 calculates an evaluation value indicating the degree of matching based on the above-described algorithm for matching point groups such as ICP, and based on the calculated evaluation value, the NDT data in step S211 is calculated. Judgment of reliability is performed.

(Modification 3)
A function corresponding to the in-vehicle device 1 may be built in the vehicle. In this case, an electronic control unit (ECU) of the vehicle executes a process corresponding to the control unit 15 of the in-vehicle device 1 by executing a program stored in the memory of the vehicle.

1 In-vehicle device 2 Server device 10 Map DB
20 Distribution map DB
11, 21 Communication unit 12, 22

Storage unit

15, 25 Control unit 13 Sensor unit 14 Input unit 16 Output unit

Claims

Measurement point cloud information on each measurement distance from the reference position to a plurality of positions, which has a difference greater than a predetermined value from the map point cloud information assigned to each of the plurality of map information areas, and is measured by the measurement unit An acquisition unit for acquiring from a plurality of moving objects;
An update unit that updates information of a region that satisfies a predetermined condition among the plurality of regions, based on a result of collation of the measurement point group information and map point group information for each of the plurality of moving objects;
An updating device.
The update unit determines an area that satisfies the predetermined condition based on a result of collating the measurement point group information and the map point group information, and updates information on the area based on the measurement point group information corresponding to the area. The updating apparatus according to claim 1.
The update unit determines a region where the evaluation values for each of the plurality of regions calculated from the comparison result of the measurement point cloud information and the map point cloud information are similar as a region satisfying the predetermined condition. The update device described in 1.
The update unit according to any one of claims 1 to 3, wherein the update unit updates information on a region that satisfies the predetermined condition based on measurement point cloud information whose evaluation value calculated from the collation result is higher than a predetermined value. Update device.
5. The update unit according to claim 1, wherein the update unit determines a region where the number of the measurement point group information for each of the plurality of regions acquired by the acquisition unit is a predetermined number or more as a region satisfying the predetermined condition. The update device according to one item.
A storage unit for storing distribution map information including map point cloud information for each of the plurality of areas;
The update device according to any one of claims 1 to 5, wherein the update unit updates map point group information of the distribution map information corresponding to an area that satisfies the predetermined condition.
The distribution map information includes reliability information related to reliability for each of the plurality of areas,
The update unit reduces the reliability indicated by the reliability information for the region, and the map information for distribution corresponding to the region satisfying the predetermined condition as the number of the measurement point cloud information acquired by the acquisition unit increases. The update apparatus according to claim 6, wherein when the map point cloud information is updated, reliability information for the region is initialized.
After generating the information of the area that satisfies the predetermined condition, the reliability of the generated information is verified based on the collation result with the measurement point group information of the area that is different from the first point cloud information used for generating the information. The update device according to any one of claims 1 to 7, further comprising a verification unit.
A control method executed by the update device,
Measurement point cloud information on each measurement distance from the reference position to a plurality of positions, which has a difference greater than a predetermined value from the map point cloud information assigned to each of the plurality of map information areas, and is measured by the measurement unit An acquisition step of acquiring from a plurality of moving objects;
An update step for updating information on a region that satisfies a predetermined condition among the plurality of regions, based on a result of collation of the measurement point cloud information and map point cloud information for each of the plurality of moving bodies;
A control method.
A program executed by a computer,
Measurement point cloud information on each measurement distance from the reference position to a plurality of positions, which has a difference greater than a predetermined value from the map point cloud information assigned to each of the plurality of map information areas, and is measured by the measurement unit An acquisition unit for acquiring from a plurality of moving objects;
A program that causes the computer to function as an update unit that updates information on a region that satisfies a predetermined condition among the plurality of regions, based on a collation result of the measurement point group information and map point group information for each of the plurality of moving objects.
A storage medium storing the program according to claim 10.