JPWO2018180338A1 - Information processing device, server device, control method, program, and storage medium - Google Patents

Abstract

The in-vehicle device 1 includes a map DB 10 including voxel data. Then, the in-vehicle device 1 calculates an evaluation function Ek for each voxel based on the matching result for each voxel based on the point cloud data measured by the rider 30 and the map DB 10. Then, the vehicle-mounted device 1 extracts a voxel whose evaluation function Ek is lower than a predetermined threshold value from the area where the evaluation function Ek is calculated, and transmits the matching reduction information D1 for the voxel to the server device 2.

Description

  The present invention relates to a point cloud matching technique.

  Conventionally, there has been known a technique of estimating a vehicle's own position by matching shape data of a peripheral object measured using a measuring device such as a laser scanner with map information in which the shape of the peripheral object is stored in advance. I have. For example, in Patent Document 1, it is determined whether a detected object in a voxel obtained by dividing a space according to a predetermined rule is a stationary object or a moving object, and matching between map information and measurement data is performed on a voxel where a stationary object exists. An autonomous mobile system for performing is disclosed.

International Publication WO2013 / 076829

  The static structure stored in the map information may have changed shape or position from the time of updating the map information, and in such a case, the matching accuracy using the map information becomes low, and as a result, The accuracy of self-position estimation will be reduced. Therefore, it is a main object of the present invention to provide an information processing apparatus capable of suitably extracting a region where matching accuracy is low.

  The invention according to claim 1 is an information processing device, wherein the first acquisition unit acquires first point group information regarding respective distances from a reference position to a plurality of positions measured by the measurement unit; A second acquisition unit configured to acquire map information in which second point group information based on a plurality of pieces of position information is recorded for each area, and a result of matching of the first point group information and the second point group information for each area A calculation unit that calculates an evaluation value for each of the regions, and an extraction unit that extracts a region in which the evaluation value is lower than a predetermined threshold among the regions in which the evaluation value is calculated. It is characterized by.

  The invention according to claim 12 is a control method executed by the information processing device, wherein the first acquisition unit acquires first point group information regarding each distance from the reference position to a plurality of positions measured by the measurement unit. A second acquiring step of acquiring map information in which second point group information based on one or a plurality of pieces of position information is recorded for each area; and a step of acquiring the first point group information and the second point group information. A calculating step of calculating an evaluation value of each area based on a comparison result of each area; and an extracting step of extracting an area in which the evaluation value is lower than a predetermined threshold value from the areas in which the evaluation value is calculated. And the following.

  The invention according to claim 13 is a program executed by a computer, wherein the first acquisition unit acquires first point group information regarding each distance from the reference position to a plurality of positions measured by the measurement unit; A second acquisition unit configured to acquire map information in which second point group information based on one or a plurality of pieces of position information is recorded for each area; and a second acquisition unit that acquires the first point group information and the second point group information for each area. A computer configured to calculate an evaluation value for each of the areas based on a result of the comparison, and an extraction unit configured to extract an area in which the evaluation value is lower than a predetermined threshold from among the areas in which the evaluation values are calculated; Is made to function.

1 is a schematic configuration of a driving support system. 2 shows a block configuration of an on-vehicle device and a server device. 4 shows an example of a schematic data structure of voxel data. A specific example of NDT scan matching will be described. A specific example of NDT scan matching in which a weight value is set for each voxel will be described. A specific example of NDT scan matching for changing a weight value will be described. It is a flowchart which shows the processing procedure which an onboard equipment performs. 9 is a flowchart illustrating a processing procedure executed by the server device.

  According to a preferred embodiment of the present invention, the information processing apparatus includes: a first acquisition unit configured to acquire first point group information regarding respective distances from the reference position to the plurality of positions measured by the measurement unit; A second acquisition unit configured to acquire map information in which second point group information based on a plurality of pieces of position information is recorded for each area, and a result of matching of the first point group information and the second point group information for each area A calculation unit that calculates an evaluation value for each of the regions based on the evaluation value, and an extraction unit that extracts, from the regions in which the evaluation values are calculated, a region in which the evaluation value is lower than a predetermined threshold. According to this aspect, the information processing apparatus preferably extracts, based on the evaluation value for each area, an area where the matching accuracy between the first point cloud information measured by the measurement unit and the second point cloud information recorded in the map information is low. can do.

  In one aspect of the information processing device, the calculation unit normalizes the evaluation value for each area based on the number of points for each area acquired as the first point group information. Accordingly, the information processing apparatus can appropriately prevent the evaluation value for each area from depending on the number of point groups measured by the measurement unit, and can appropriately extract an area with relatively low matching accuracy.

  In another aspect of the information processing device, the information processing device further includes a position estimating unit that estimates the reference position, and the map information includes a weighting value based on the reliability of the second point cloud information. The reference position is included for each region, and the position estimating unit estimates the reference position based on a matching result for each region based on a weight value for each region. According to this aspect, the information processing apparatus can perform high-accuracy position estimation by increasing the weight of the matching result of the region with high reliability.

  In another aspect of the information processing device, the position estimating unit may include a parameter having the highest evaluation indicated by an overall evaluation value that is a sum of evaluation values calculated for each region from the comparison result and the weighting value. Is used to estimate the reference position. According to this aspect, the information processing apparatus can appropriately determine a parameter for determining the reference position in consideration of the weight value for each area, and perform highly accurate position estimation.

  In another aspect of the information processing apparatus, the map information includes information on the number of points for each area in the second point group information, and the extraction unit includes the map information. A region to be extracted is determined based on the information on the number of points for each region and the number of points for each region acquired as the first point group information. Generally, for an area in which only a small number of points can be measured as compared with the number of points registered in the map information, accurate matching may not be performed regardless of the evaluation value. Therefore, in this aspect, the information processing apparatus can appropriately extract an area in which the number of measured points is small as an area in which the matching accuracy is relatively low.

  In another aspect of the information processing device, a transmitting unit that transmits region information on the region extracted by the extracting unit to a server device that manages map information in which second point group information is recorded for each region. Is further provided. As described above, the information processing device can suitably provide the server device with information useful for updating the map information.

  In another aspect of the information processing device, the transmission unit transmits, to the server device, region information on a region in which a predetermined number or more of the regions extracted by the extraction unit are adjacent to each other. According to this aspect, the information processing apparatus transmits only the effective information to the server apparatus while suppressing the transmission frequency by transmitting the area information regarding the area where the static object change is highly likely to have occurred to the server apparatus. Can be provided. Here, “change of a static object” includes deformation, movement, disappearance, occurrence, and the like of the static object.

  In another aspect of the information processing device, the transmission unit may include: area information regarding all the areas extracted by the extraction unit; and an area regarding an area where a predetermined number or more of the areas extracted by the extraction unit are adjacent to each other. One of the information is selectively transmitted to the server device. According to this aspect, the information processing apparatus can provide information according to need to the server apparatus.

  According to another preferred embodiment of the present invention, the server device includes: a receiving unit that receives region information from a plurality of information processing devices; and a plurality of region information for each region received by the receiving unit. A determination unit for determining, for each of the regions, whether or not the region is a region in which a change of an object has occurred. According to this aspect, the server device collects, from a plurality of information processing devices, region information of a region that may be a region where a static object change has occurred, and statistically determines a region in which map information needs to be updated. Can be identified.

  In one aspect of the server device, the server device transmits, to the information processing device, a request to transmit measurement data from the measurement unit to the region determined by the determination unit to be a region where a static change in the object has occurred. And a transmission unit that performs the transmission. According to this aspect, the server device can appropriately collect the measurement data of the area determined to be the area where the change of the static object has occurred and use it for updating the map information.

  In another aspect of the server device, the server device further includes a storage unit that stores a weight value related to matching for each region, and the region information is received by the receiving unit for a predetermined number or more of the region information. Decrease the weight value. According to this aspect, the server device can lower the weighting value of the region that is highly likely to be the region where the static object has changed, and appropriately improve the position estimation accuracy when using the weighting value. it can.

  According to another preferred embodiment of the present invention, there is provided a control method executed by an information processing apparatus, wherein first point group information regarding a distance from a reference position to a plurality of positions measured by a measurement unit is acquired. A first acquisition step of acquiring map information in which second point group information based on one or a plurality of pieces of position information is recorded for each area, the first point group information and the second point A calculating step of calculating an evaluation value for each area based on a comparison result for each area of the group information, and, among the areas where the evaluation value is calculated, an area where the evaluation value is lower than a predetermined threshold value; Extracting step. By executing this control method, the information processing apparatus can appropriately extract an area where the matching accuracy between the first point cloud information measured by the measurement unit and the second point cloud information recorded in the map information is low. it can.

  According to another preferred embodiment of the present invention, there is provided a program executed by a computer, the first program acquiring first point group information relating to respective distances from a reference position to a plurality of positions measured by a measurement unit. An acquisition unit, a second acquisition unit that acquires map information in which second point group information based on one or a plurality of pieces of position information is recorded for each area, and an acquisition unit that acquires the first point group information and the second point group information. A calculation unit that calculates an evaluation value for each area based on the result of matching for each area, and an extraction that extracts an area in which the evaluation value is lower than a predetermined threshold among the areas for which the evaluation value is calculated The computer functions as a unit. By executing this program, the computer can appropriately extract an area where the matching accuracy between the first point cloud information measured by the measurement unit and the second point cloud information recorded in the map information is low. Preferably, the program is stored in a storage medium.

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

[Overview of driving support system]
FIG. 1 is a schematic configuration of the driving support system according to the present embodiment. The driving support system includes an in-vehicle device 1 that moves 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. However, actually, a plurality of sets of the in-vehicle device 1 and the vehicle exist at different positions.

  The in-vehicle device 1 is electrically connected to an external sensor such as a lidar (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. Then, the on-vehicle device 1 performs automatic driving control of the vehicle based on the estimation result of the own vehicle position so as to travel along the set route to the destination. The in-vehicle device 1 stores a map database (DB: DataBase) 10 including voxel data. The voxel data is data in which position information and the like of a stationary structure are 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 obtained by expressing a measured point group data of a stationary structure in each voxel by a normal distribution, and is used for scan matching using NDT (Normal Distributions Transform) as described later.

  The in-vehicle device 1 performs scan matching based on the NDT based on the point cloud data output by the rider and the voxel data corresponding to the voxel to which the point cloud data belongs. Then, when detecting a voxel with low matching accuracy, the on-vehicle device 1 transmits information about the voxel (also referred to as “matching decrease information D1”) to the server device 2. Further, when the on-vehicle device 1 receives information (also referred to as a “request signal D2”) specifying a specific voxel from the server device 2, measurement data (“measurement data D3”) by a rider or the like within the specified voxel is received. ) Is transmitted to the server device 2. The on-vehicle device 1 is an example of the “information processing device” in the present invention. Further, the matching reduction information D1 is an example of “region information” in the present invention.

  The server device 2 performs data communication with the vehicle-mounted device 1 corresponding to a plurality of vehicles. The server device 2 stores a distribution map DB 20 for distribution to the vehicle-mounted devices 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 drop information D1 received from the on-vehicle device 1, and determines whether or not it is necessary to update voxel data for a specific voxel based on the stored matching drop information D1. Then, when the server device 2 determines that the voxel data for a specific voxel needs to be updated, the server device 2 transmits a request signal D2 specifying the voxel to each vehicle-mounted device 1. Then, the server device 2 performs a process of updating the target voxel data based on the measurement data D3 received from the vehicle-mounted device 1 as a response to the request signal D2.

[Configuration of onboard equipment]
FIG. 2A is a block diagram illustrating a functional configuration of the vehicle-mounted device 1. As shown in FIG. 2A, the on-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 interconnected via a bus line.

  The communication unit 11 receives the map information distributed from the server device 2 and transmits the matching reduction information D1 generated by the control unit 15 to the server device 2 based on the control of the control unit 15. In addition, when receiving the request signal D2, the communication unit 11 transmits the measurement data D3 to the server device 2 based on the control of the control unit 15. In addition, the communication unit 11 transmits a signal for controlling the vehicle to the vehicle, and receives a signal regarding the state of the vehicle from the 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 angle range in the horizontal direction and the vertical direction, thereby discretely measuring a 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 rider 30 includes an irradiation unit that irradiates laser light while changing the irradiation direction, a light receiving unit that receives reflected light (scattered light) of the irradiated laser light, and scan data based on a light reception signal output by the light receiving unit. And an output unit that outputs The scan data is generated based on the irradiation direction corresponding to the laser light received by the light receiving unit and the response delay time of the laser light specified based on the above-described light reception signal. The rider 30 is an example of the “measurement unit” in the present invention, and the point cloud data output by the rider 30 is an example of the “first point cloud information” in the present invention.

  The input unit 14 is a button for a user to operate, a touch panel, a remote controller, a voice input device, and the like, and receives an input for designating a destination for a route search, an input for designating ON and OFF of automatic driving, and the like. , And supplies the generated input signal to the control unit 15. The output unit 16 is, for example, a display, a speaker, or the like that performs output under 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 performs the scan matching based on the NDT based on the point cloud data output from the rider 30 and the voxel data corresponding to the voxel to which the point cloud data belongs, thereby estimating the position of the own vehicle. Do. Further, the control unit 15 detects a voxel estimated to have low matching accuracy based on the evaluation value for each voxel obtained by scan matching based on the NDT. Then, the control unit 15 generates matching decrease information D1 corresponding to the detected voxel, and transmits the matching decrease information D1 to the server device 2 by the communication unit 11. 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 control unit 15 converts the point cloud data output from the rider 30 into the point cloud data belonging to the voxel. Is extracted and transmitted to the server device 2 as the measurement data D3. The control unit 15 is an example of a “first acquisition unit”, a “second acquisition unit”, a “calculation unit”, an “extraction unit”, a “position estimation unit”, and a “computer” that executes a program in the present invention.

[Configuration of server device]
FIG. 2B shows a schematic configuration of the server device 2. As shown 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 mutually connected via a bus line.

  The communication unit 21 communicates various data with the vehicle-mounted 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. Further, the storage unit 22 stores the distribution map DB 20, and also stores the matching reduction 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 reduction information D1 received from the on-vehicle device 1 in the storage unit 22, and determines whether the voxel data needs to be updated for a specific voxel based on the accumulated matching reduction information D1. . Then, when the control unit 25 determines that the voxel data for a specific voxel needs to be updated, the control unit 25 transmits a request signal D2 specifying the voxel to each on-vehicle device 1 through the communication unit 21. Then, the control unit 25 performs processing for updating voxel data included in the distribution map DB 20 based on the measurement data D3 received from the on-vehicle device 1 based on the request signal D2. The control unit 25 is an example of the “receiving unit”, the “transmitting unit”, and the “determining unit” 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 a point group in the voxel is represented by a normal distribution. In the present embodiment, as shown in FIG. 3, a voxel ID, voxel coordinates, an average vector, a covariance matrix , Weight values, and point group number information. Here, the “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 and has a predetermined shape and size, so that the space of each voxel can be specified by 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 a target voxel is represented by a normal distribution, and an arbitrary vector in an arbitrary voxel “k”. Of the point "i"

と定義し、ボクセルｋ内での点群数を「Ｎ_ｋ」とすると、ボクセルｋでの平均ベクトル「μ_ｋ」及び共分散行列「Ｖ_ｋ」は、それぞれ以下の式（１）及び式（２）により表される。

Assuming that the number of point groups in voxel k is “N _k ”, the mean vector “μ _k ” and covariance matrix “V _k ” in voxel k are represented by the following equations (1) and ( 2).

なお、ボクセルデータに含まれる平均ベクトル及び共分散行列は、本発明における「第２点群情報」の一例である。

Note that the average vector and the covariance matrix included in the voxel data are an example of “second point group information” in the present invention.

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

(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 the NDT scan matching by the number of point groups measured in the voxel, and includes the value in the voxel data. The weight is calculated using the weight value to be calculated. Accordingly, the on-vehicle device 1 accurately specifies voxels having relatively low scan matching accuracy based on the evaluation value, and preferably improves the position estimation accuracy based on the NDT scan matching.

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

「ｔ_ｘ」は、ｘ方向の移動量を示し、「ｔ_ｙ」は、ｙ方向の移動量を示し、「Ψ」は、ｘｙ平面内での回転角（即ちヨー角）を示す。なお、垂直方向移動量、ピッチ角、ロール角は、道路勾配や振動によって生じるものの、無視できる程度に小さい。

“T _x ” indicates the amount of movement in the x direction, “t _y ” indicates the amount of movement in the y direction, and “を” indicates the rotation angle (ie, yaw angle) in the xy plane. Note that the vertical movement amount, pitch angle, and roll angle are negligibly small, although they are caused by road gradients and vibrations.

When the coordinates [x _k (i), y _k (i), z _k (i)] ^T of an arbitrary point in the point cloud data obtained by the rider 30 are coordinate-transformed using the above-described estimation parameter P, coordinates _{"x 'k} (i)" is expressed by the following equation (3).

Then, in the present embodiment, the vehicle-mounted device 1 uses the coordinate-transformed point group, the average vector μ _k and the covariance matrix V _k included in the voxel data to calculate the voxel k represented by the following equation (4). The evaluation function “E _k ” and a comprehensive evaluation function “E” (also referred to as “overall evaluation function”) for all the voxels to be matched represented by Expression (5) are calculated.

「Ｍ」は、マッチングの対象となるボクセルの数を示し、「ｗ_ｋ」は、ボクセルｋに対する重み付け値を示す。なお、ライダ３０により得られる点群データの座標は、自車位置に対する相対座標であり、ボクセルデータの平均ベクトルは絶対座標であることから、式（４）を算出する際には、例えば、ライダ３０により得られる点群データの座標を、ＧＰＳ受信機３２の出力等から予測した自車位置に基づき座標変換する。

“M” indicates the number of voxels to be matched, and “w _k ” indicates a weight value for voxel k. Note that the coordinates of the point cloud data obtained by the rider 30 are relative coordinates with respect to the own vehicle position, and the average vector of the voxel data is absolute coordinates. 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 and the like.

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

As is clear from the comparison between Expressions (4) and (6), in the present embodiment, the in-vehicle device 1 normalizes the evaluation function E _k using the number N _k of point groups. 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 weight value corresponding to the reliability of each voxel data (mean vector, covariance matrix). Thus, the vehicle-mounted device 1, the weighting of the evaluation function E _k of unreliable voxels relatively low, thereby suitably improving the position estimation accuracy of NDT matching.

  After that, the on-vehicle device 1 calculates an estimation parameter P by which the total evaluation function E is maximized by an arbitrary root finding algorithm such as Newton's method. Then, 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, a case of a two-dimensional plane will be described as an example for convenience of explanation.

  FIG. 4A shows circles indicating point groups measured by a rider or the like in four adjacent voxels “B1” to “B4” when the vehicle is driven by a measurement and maintenance vehicle for creating a map. FIG. 5 is a diagram showing a two-dimensional normal distribution created from Expressions (1) and (2) based on gradation based on gradation. The mean and variance of the normal distribution shown in FIG. 4A correspond to the mean vector and the covariance matrix in the voxel data, respectively.

  FIG. 4B is a diagram in which the point cloud obtained by the rider 30 while the in-vehicle device 1 is traveling in FIG. 4A is indicated by an asterisk. The position of the point cloud of the rider 30 indicated by the star is aligned with each of the voxels B1 to B4 based on the estimated position based on the output of the GPS receiver 32 and the like. In the example of FIG. 4B, a deviation occurs between the point cloud (circled) measured by the measurement and maintenance vehicle and the point cloud (star) acquired by the on-vehicle device 1.

  FIG. 4C is a diagram illustrating a state after the point cloud (star mark) acquired by the vehicle-mounted device 1 based on the matching result of the NDT scan matching is moved. In FIG. 4C, based on the mean and variance of the normal distribution shown in FIGS. 4A and 4B, a parameter P that maximizes the evaluation function E shown in equation (4) is calculated, and the calculated parameter P Is applied to the point cloud of the star shown in FIG. In this case, the deviation between the point cloud (circled) measured by the measurement and maintenance vehicle and the point cloud (star) acquired by the on-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 = 1.290
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 the voxels. In this example, the evaluation function E1 of the voxel B1 having a large number of point groups is large.

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, the weighting values for the voxels B1 to B4 are all “1”.
E1 = 0.1208
E2 = 0.1136
E3 = 0.1233
E4 = 0.1211
E = 0.4789

  In this case, since the evaluation functions E1 to E4 and the comprehensive evaluation function E are values that are hardly affected by the number of point groups in the voxel, the degree of matching between the voxels can be easily compared.

  In this embodiment, a weight value is set for each voxel. Therefore, by increasing the weight of voxels with high reliability, the degree of matching of the voxels can be increased.

  FIG. 5A is a diagram illustrating a matching result when weighting values for all the 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 weight value of the voxel B1 is set to be 10 times the weight value of the other voxels. FIG. 5C is a diagram illustrating a matching result when the weight value of the voxel B3 is set to be ten times the weight value of another voxel.

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

  As described above, in the example of FIG. 5B, matching is performed such that the value of the evaluation function E1 corresponding to the voxel B1 increases, and the degree of matching in the voxel B1 is increased. Therefore, the deviation between the circle mark and the star mark of the voxel B1 is reduced.

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 increases, 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 vehicle device 1 of the matching decrease information is based on the evaluation function E _k and synthetic evaluation function E, it is (need have That update delivery map DB 20) possibly a change occurs in the static structure The voxel is specified, and information about the voxel is transmitted to the server device 2 as matching reduction information D1.

In general, when the overall evaluation function E or the evaluation function _Ek decreases (that is, when the matching degree decreases), the following three events occur:
(A) Voxel contains a dynamic object
(B) There are many errors in the predicted own vehicle position, and accurate matching has not been performed.
(C) It is presumed that either of the static objects in the voxel has changed (including generation and disappearance). Here, in the present embodiment, since the evaluation function E _k is normalized by the number of point groups N _k , each evaluation function E _k and the total evaluation function E which is the sum thereof are affected by the number of point groups 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 having occurred. Therefore, when detecting such a voxel k, the in-vehicle device 1 transmits the matching reduction information D1 on the voxel k to the server device 2. Here, the matching reduction information D1 includes, for example, time information, estimated own vehicle position information, overall evaluation function E, evaluation function E _k , voxel ID, and point group number Nk. Thereafter, based on the plurality of pieces of matching reduction information D1 on the target voxel k received from the in-vehicle devices 1 of the plurality of vehicles, the server device 2 assigns either the event (a) or the event (c) to the voxel k by a statistical method. Determine if it has occurred.

Here, the evaluation of the function E _k, an example of a method for detecting a small evaluation function E _k as compared with other evaluation functions E _k. The vehicle-mounted device 1 determines that the value obtained by dividing the evaluation function E _k by a reference value “F _k ”, which will be described later, is smaller than a predetermined value “A”, that is, the conditional expression
E _k / F _k <A Equation (7)
There If satisfied, the evaluation function E _k is determined to be relatively small compared to the other evaluation functions E _k, and transmits a matching reduction information D1 for voxels of the evaluation function E _k to the server apparatus 2. Expression (7) is an expression equivalent to the following expression (8).
E _k <A · F _k equation (8)

Here, the vehicle-mounted device 1, the reference value F _k in the above-mentioned judgment formula is calculated based on the following equation (9).

式（９）に示すように、基準値Ｆ_ｋは、対象となるボクセルｋの重み付け値ｗ_ｋに基づく重み付けがなされた総合評価関数Ｅに相当する。このようにすることで、車載機１は、他の評価関数Ｅ_ｋと比べて相対的に小さい評価関数Ｅ_ｋを好適に検出することができる。なお、式（８）の「Ａ・Ｆ_ｋ」は、本発明における「所定の閾値」の一例である。

As shown in the equation (9), the reference value F _k corresponds to the comprehensive evaluation function E 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. Note that “A · F _k ” in Expression (8) is an example of the “predetermined threshold” in the present invention.

In addition, in addition to or instead of detecting the evaluation function E _k that is smaller than the other evaluation functions E _k , the in-vehicle device 1 detects a voxel having a small number of point groups N _k in the voxel. 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, when detecting the voxel in which the number of point clouds _Nk is smaller than the predetermined threshold, the on-vehicle device 1 determines that there is a high possibility that either the event (a) or the event (c) has occurred, and The matching reduction information D1 for voxels is transmitted to the server device 2.

In this case, preferably, the vehicle-mounted device 1 refers to the point group number information of the voxel data, and sets the above-described threshold according to the point group number information. In this case, the in-vehicle device 1 sets the above-described threshold smaller as the number of point groups indicated by the point group number information is smaller. Thus, the on-vehicle device 1 can suitably determine whether or not the number Nk of point groups is smaller than the number of point groups to be originally acquired, based on the above-described threshold. Incidentally, the vehicle-mounted device 1, even if it detects the voxel k as the smallest evaluation function E _k as compared with other evaluation functions E _k based on equation (7) or the like, and the number groups point in the voxel k When Nk is smaller than the threshold value, the matching reduction 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 reduction information D1 received from the in-vehicle devices 1 of a plurality of vehicles for each voxel ID, and selects one of the event (a) or the event (c) described above for each voxel ID. Is determined statistically.

  For example, when there is a voxel in which the number of pieces of matching reduction information D1 for each voxel ID is stored in a predetermined number or more, the server device 2 has a high possibility that the event (c) has occurred and the voxel corresponds to the voxel. It is determined that the reliability of the voxel data is low. Therefore, the server device 2 lowers the weight value included in the voxel data corresponding to the voxel. At this time, it is preferable that the server device 2 set the weighting value to be lower as the number of matching lowering information D1 is larger. By doing so, the server device 2 reduces the weight of the voxel in which the event (c) is highly likely to occur, and performs in-vehicle device estimation based on the map information distributed from the server device 2. 1 to improve the position estimation accuracy.

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

  FIG. 6A is a diagram illustrating a point cloud (star) 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) acquired by the measurement and maintenance vehicle at the time when the voxel data is created before the 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 clouds acquired by the rider 30 in the voxel 3 decreases, or the point acquired by the measurement and maintenance vehicle is reduced. A point cloud deviating from the group may be acquired by the rider 30. The same applies when a dynamic object enters a position in the voxel B3.

FIG. 6B is a diagram illustrating a matching result when the weighting values for the voxels B1 to B4 are all equal. Due to the influence of the shift of the voxel B3, the shift between the circle mark and the star mark of the voxels B1, B2, and B4 also increases. 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 = 0.426

In this case, the value of the evaluation function E3 of the voxel B3 is smaller than the others. When calculating equation (9),
F1 = F2 = F3 = F4 ≒ 0.1007
Therefore, the left side of Expression (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 of Expression (7). Therefore, in this case, the vehicle-mounted device 1 determines that the evaluation function E3 is relatively smaller than the other evaluation functions, and transmits the matching reduction information D1 on the voxel B3 to the server device 2.

FIG. 6C shows a matching result after changing the weight value of the voxel B3. In the example of FIG. 6C, the server device 2 sets the weight value of the voxel B3 to 1/10 based on the matching reduction information D1 of the voxel B3 transmitted from the in-vehicle devices 1 of a plurality of vehicles. As a result, the influence of the displacement of the voxel B3 is reduced, and the displacement between the circle and the star of the voxels B1, B2, and 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

As described above, when the weight value for the voxel B3 is set to 1/10, matching is performed so as to increase the degree of matching for voxels other than the voxel B3. As a result, it is possible to perform position estimation with a low degree of influence of the voxel B3 having low reliability. In this case, when equation (9) is calculated,
F1 = F2 = F4 ≒ 0.1493, F3 ≒ 0.0149
Therefore, the left side of Expression (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 reduction information D1 on the voxel B3 is transmitted to the server device 2 also in this case.
As described above, since the evaluation function E _k is normalized by the reference value F _k , it is possible to make a condition determination that is not affected by the weight value.

(3) Voxel data correction processing The server device 2 determines whether it is necessary to correct voxel data such as an average vector and a covariance matrix of the voxel whose weight has been reduced by the weight change processing described above.

  In the first example, the 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 based on an image obtained by capturing the voxel of interest.

  In this case, the server device 2 periodically receives, for example, an image captured from a running vehicle from the vehicle-mounted device 1 together with the position information and the time information and accumulates the image in the storage unit 22, and performs correction of the voxel data. When performing the necessity determination, an image in which the target voxel is captured is extracted from the storage unit 22. Then, the server device 2 determines whether or not the voxel data needs to be corrected based on the extracted image. For example, the server device 2 compares the image captured when the voxel data was generated last time with the latest image, and determines that a change has occurred in the shape, position, or the like of the feature displayed in the image. When it is determined, it is determined that the voxel data needs to be corrected.

In a second example, the server apparatus 2 judges whether or not the similarity evaluation function E _k included in the voxel matching reduction information D1 of interest. Then, the server apparatus 2 determines that the evaluation function E _k included in each matching decrease information D1 if it is determined to be similar, it is necessary to modify the voxel data for the voxel of interest. In this case, the server device 2, compares the presence or absence of similarity mentioned above, for example, calculates an index value equivalent to variance or dispersion evaluation function E _k included in the matching decrease information D1, and the calculated value with a predetermined threshold value May be determined.

  Then, when there is a voxel for which it is determined that the voxel data needs to be corrected, the server device 2 transmits a request signal D2 specifying the voxel to each vehicle-mounted device 1. Then, the server device 2 corrects the voxel data based on the measurement data D3 received as a response to the request signal D2. A specific example of this processing will be specifically described in the section of [Processing Flow].

[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 the present embodiment. The on-vehicle device 1 repeatedly executes the processing of the flowchart in FIG.

  First, the in-vehicle device 1 sets an initial value of the own vehicle position based on the output of the GPS receiver 32 and the like (step S101). Next, the in-vehicle device 1 acquires the vehicle speed from the speed sensor 34 and the angular velocity in the yaw direction from the gyro sensor 33 (step S102). Then, the on-vehicle device 1 calculates the moving distance of the vehicle and the change in the bearing of the vehicle based on the acquisition result of Step S102 (Step S103).

Thereafter, the on-vehicle device 1 calculates the predicted position by adding the moving distance and the azimuth change calculated in step S103 to the estimated own vehicle position one time ago (step S104). Then, the vehicle-mounted device 1 acquires voxel data of voxels existing around the own vehicle position with reference to the map DB 10 based on the predicted position calculated in step S104 (step S105). Further, the on-vehicle device 1 divides the scan data obtained from the rider 30 into voxels based on the predicted position calculated in step S104 (step S106). Then, the on-vehicle device 1 calculates the NDT scan matching using the evaluation function (Step S107). In this case, the in-vehicle device 1 calculates the evaluation function _Ek and the total evaluation function E based on the equations (4) and (5), and calculates the estimation parameter P at which the total evaluation function E is maximized.

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) based on, to calculate the reference value _{F k} (step S109). Then, the on-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 vehicle-mounted unit 1, i.e. determines whether the evaluation function E _k satisfying the equation (7) is present.

If there is a voxel whose comparison result is smaller than the predetermined value A (step S111; Yes), the in-vehicle device 1 determines the time, the estimated position, the comprehensive evaluation function E, the voxel ID of the target voxel, the evaluation function E _k , And the matching reduction information D1 including the point cloud number _Nk to the server device 2 (step S112). Instead of or in addition to the determination in step S111, the in-vehicle device 1 determines the number of point groups _Nk based on the magnitude of the point cloud number _Nk , as described in the section “(1) Transmission processing of matching reduction information ”. The necessity of transmission of the matching decrease information D1 may be determined.

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

  Next, in step S113, the vehicle-mounted device 1 determines whether or not the request signal D2 designating a voxel around the own vehicle position has been received from the server device 2 (step S113). When the in-vehicle device 1 receives the request signal D2 specifying the voxel around the own vehicle position from the server device 2 (step S113; Yes), the scan data of the rider 30 corresponding to the voxel specified by the request signal D2 ( The point group data) is transmitted to the server device 2 as the measurement data D3 (step S114). At this time, the in-vehicle device 1 may include, in addition to the scan data, the comprehensive evaluation function E of the time at which the scan data was acquired and the like in the measurement data D3. The comprehensive evaluation function E included in the measurement data D3 is used in processing of the server device 2 described later. Then, after the execution of step S114, or when determining in step S113 that the request signal D2 designating a voxel around the own vehicle position has not been received, the in-vehicle device 1 returns the process to step S102.

(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 processing of the flowchart in FIG.

  First, the server device 2 receives the matching reduction information D1 from the vehicle-mounted device 1 mounted on the vehicle (step S201). Then, the server device 2 stores the matching reduction information D1 in the storage unit 22 for each 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 pieces of matching reduction information D1 is larger than a predetermined value (Step S202). Then, when there is a voxel in which the number of pieces of matching deterioration information D1 is larger than a predetermined value (step S202; Yes), the server device 2 stores the voxel in the distribution map DB20 of the corresponding voxel as the number of pieces of matching deterioration information D1 increases. the weighting value _{w k} which is smaller sets (step S203). The initial value of the weighting values w _k stored in the delivery map DB20 is set to, for example, a common initial value in each voxel (e.g., 1).

Next, the server device 2 determines whether or not the voxel data of the voxel whose weight value w _k has been reduced should be corrected (step S204). In this case, as described in the section of “(3) Voxel data correction processing ”, the server device 2 may make the above-described determination based on an image obtained by capturing the target voxel. based on the presence or absence of similarity evaluation function E _k included in the matching decrease information D1 may perform the determination described above.

  Then, when determining that the voxel data should be corrected (Step S204; Yes), the server device 2 transmits a request signal D2 requesting scan data of the target voxel to the in-vehicle device 1 of each vehicle (Step S204). S205). Then, the server device 2 receives the measurement data D3 including the scan data of the target voxel from the vehicle-mounted device 1 of each vehicle as a response to the request signal D2, and stores the measurement data D3 in the storage unit 22 (Step S206). The measurement data D3 includes, in addition to the scan data, a comprehensive evaluation function E of the time at which the scan data was acquired, and the like. On the other hand, when the server device 2 determines that the voxel data does not need to be corrected (Step S204; No), the process returns to Step S201.

  Next, the server device 2 determines whether or not the measurement data D3 having a high total evaluation function E has been accumulated for the voxel for which it has been determined in Step S204 that the voxel data should be corrected (Step S207). Specifically, the server device 2 determines whether or not a predetermined number or more of measurement data D3 whose overall evaluation function E is higher than a predetermined threshold is accumulated. Generally, when the comprehensive evaluation function E is high, the vehicle position can be estimated with high accuracy, and it is estimated that the reliability of the measurement data used for calculating the comprehensive evaluation function E is high. In consideration of the above, the server device 2 performs the determination process of step S207 to determine whether the measurement data D3 necessary for updating the voxel data has been collected.

  Then, when the measurement data D3 having the high overall evaluation function E is accumulated (Step S207; Yes), the server device 2 converts the point group data of the target voxel by weighted averaging based on the value of the overall evaluation function E. It is constructed (step S208). Accordingly, when constructing the point voxel data of the target voxel, the server device 2 increases the weight for the scan data with higher reliability, and constructs highly accurate point cloud data. On the other hand, when the measurement data D3 having the high total evaluation function E is not stored (Step S207; No), the server device 2 returns the process to Step S201. Note that, in this case, the server device 2 outputs a predetermined warning to the administrator, and informs the administrator that there is a voxel whose voxel data is to be corrected and causes the measurement maintenance vehicle to travel on a road in which the voxel is within the measurement range. The administrator may be notified that 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 group data constructed in step S208 (step S209). Then, the server device 2 updates the target voxel data in the distribution map DB 20 based on the processing result of step S209 (step S210). Preferably, between step S209 and step S210, a step for verifying whether the data of the NDT generated in step S209 has no problem is provided. Then, the server device 2 sets the weight value of the voxel whose voxel data has been updated to the initial value (step S211).

As described above, the in-vehicle device 1 according to the present embodiment includes the map DB 10 including voxel data. The vehicle unit 1, based on the comparison result of each voxel by the rider 30 and the point cloud data and the map DB10 measured, to calculate the evaluation function E _k for each voxel. The vehicle unit 1, of the evaluation function E _k is calculated area, the evaluation function E _k extracts the voxels below a predetermined threshold, and transmits a matching reduction information D1 with respect to the voxel to the server apparatus 2. Thereby, the vehicle-mounted device 1 can suitably provide the server device 2 with information on voxels having low matching accuracy.

[Modification]
Hereinafter, a modified example suitable for the embodiment will be described. The following modifications may be combined and applied to the embodiment.

(Modification 1)
The configuration of the driving support system shown in FIG. 1 is an example, and the configuration to which the present invention can be applied is not limited to this. For example, the driving support system does not have the server device 2 and the in-vehicle device 1 may execute the processing of the server device 2.

  In this case, the vehicle-mounted device 1 stores the matching reduction information D1 in the storage unit 12 instead of transmitting the matching reduction information D1 to the server device 2 in step S112 of FIG. Then, when the number of pieces of matching reduction information D1 for the same voxel becomes larger than a predetermined value due to the vehicle passing the same place a plurality of times, the in-vehicle device 1 determines the target as in step S203 of FIG. Change voxel weights. The in-vehicle device 1 also stores the measurement data of the rider 30 corresponding to the voxel that generated the matching drop information D1 together with the matching drop information D1, and stores it when it is determined that the voxel data should be corrected. The processing of steps S207 to S211 in FIG. 8 is executed based on the measurement data and the comprehensive evaluation function E included in the matching reduction information D1. Also in this mode, the on-vehicle device 1 can appropriately update the voxel data.

(Modification 2)
When transmitting the matching reduction information D1 in step S112 of FIG. 7, the in-vehicle device 1 may transmit the measurement data of the rider 30 for the target voxel to the server device 2 together with the matching reduction information D1. According to this aspect, the server device 2 can obtain information corresponding to the measurement data D3 without transmitting the request signal D2.

(Modification 3)
When the comprehensive evaluation function E specified in step S108 of FIG. 7 is lower than the predetermined value, the on-vehicle device 1 transmits the information on the estimated own vehicle position calculated using the estimation parameter P or the like to the server device 2. Is also good.

  In this case, the server device 2 collects information indicating the estimated own vehicle position when the comprehensive evaluation function E is lower than a predetermined value (also referred to as “low-accuracy position information”) from the vehicle-mounted device 1 of each vehicle, If a predetermined number or more of low-accuracy position information indicating positions within a predetermined distance is accumulated, it is determined that there is some cause of poor matching near the position indicated by the low-accuracy position information. Then, in this case, similarly to step S205 in FIG. 8, the server device 2 transmits a request signal D2 designating a voxel near the position indicated by the low-accuracy position information to the on-vehicle device 1 of each vehicle. Then, based on the measurement data D3 received as a response to the request signal D2, the server device 2 updates the voxel data near the position indicated by the low-accuracy position information in the same procedure as in steps S208 to S211 in FIG. Do. According to this aspect, the server device 2 can appropriately update the voxel data around the position where the matching accuracy is low.

(Modification 4)
A function corresponding to the on-vehicle device 1 may be built in the vehicle. In this case, the electronic control unit (ECU: Electronic Control Unit) of the vehicle executes a program corresponding to the control unit 15 of the on-vehicle device 1 by executing a program stored in the memory of the vehicle.

(Modification 5)
Voxel data is not limited to a data structure including a mean vector and a covariance matrix, as shown in FIG. For example, the voxel data may directly include the point cloud data measured by the measurement and maintenance vehicle used when calculating the average vector and the covariance matrix. In this case, the point cloud data included in the voxel data is an example of the “second point cloud information” in the present invention.

(Modification 6)
Vehicle device 1, the evaluation function E _k is of lower voxel than a predetermined threshold value, it may transmit a matching reduction information D1 for each of voxels predetermined number or more adjacent to the server apparatus 2.

In this case, the control unit 15 of the in-vehicle device 1, when transmitting a matching reduction information D1 in step S112 of FIG. 7, the communication unit matching reduction information D1 evaluation function E _k is for all the lower voxel than a predetermined threshold 11 instead of being sent to, the voxel evaluation function E _k is lower than a predetermined threshold (also referred to as "low-confidence voxels".) constituted voxels adjacent to each other more than a predetermined number to extract, the The communication unit 11 transmits the matching reduction information D1 for each of the voxels included in the low reliability voxel group to the server device 2.

When the evaluation function E _k is a number of voxels below a predetermined threshold are present adjacent to each other, because it is likely that the event (c) has occurred, for such voxel of the voxel data Updates are likely to be required. On the other hand, the evaluation function E _k voxel lower than a predetermined threshold, for if present adjacent either alone or in only a few, is considered likely event (a) occurs, like this For voxels, it is relatively unlikely that voxel data needs to be updated. That is, the necessity of updating voxel data can be determined based on the size of the low-reliability voxel group. Therefore, the predetermined number, which is a threshold value for determining a low-reliability voxel group, is set experimentally or empirically, for example, in consideration of a predetermined voxel size.

  According to this aspect, the in-vehicle device 1 transmits the matching reduction information D1 for the voxel in which the possibility of the static object change is high to the server device 2 to the server device 2, thereby suppressing the transmission frequency and effective information. Alone can be transmitted to the server device 2. In addition, since the in-vehicle device 1 transmits the matching reduction information D1 for the voxel that is likely to need to update the voxel data, the server device 2 performs the processes in step S202 and step S204 in FIG. This can be omitted, and the processing load on the server device 2 can be reduced.

Alternatively, the in-vehicle device 1 may be capable of changing the range of the matching reduction information D1 transmitted to the server device 2 based on a request from the server device 2. For example, the in-vehicle device 1 sets the range of the matching reduction information D1 to be transmitted to the server device 2 to all voxels whose evaluation function E _k is lower than a predetermined threshold or the evaluation function E in response to a request from the server device 2. A predetermined number or more of voxels whose _k is lower than a predetermined threshold are changed to any of adjacent voxels (that is, voxels constituting a low reliability voxel group).

In this case, the server device 2 transmits the matching reduction information D1 to all the voxels whose evaluation function _Ek is lower than a predetermined threshold in an area where a large amount of information is to be collected in order to examine the reliability of the voxel data. In an area where the reliability of voxel data does not need to be closely examined, at least a predetermined number of voxels whose evaluation function E _k is lower than a predetermined threshold are adjacent voxels (that is, low reliability). The in-vehicle device 1 is requested to transmit the matching reduction information D1 for each voxel constituting the voxel group). With such a configuration, the server device 2 can acquire information suitable for each of the area where the scrutiny of the reliability of the voxel data is required and the area where the scrutiny is not required. That is, the in-vehicle device 1 can provide information according to need to the server device.

Further, the present embodiment is not limited to the scan matching by the NDT, and another scan matching such as an ICP (Iterative Closest Point) may be applied. Even in this case, as in the embodiment, the on-vehicle device 1 normalizes the evaluation function for each voxel for evaluating the degree of matching by the number of point groups _Nk . Then, the on-vehicle device 1 specifies a voxel having a relatively low matching degree, and transmits the matching reduction information D1 for the voxel to the server device 2. As described above, the scan matching method applicable to the present invention is not limited to the NDT scan matching.

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

The invention according to claim 1 is an information processing device, wherein the first acquisition unit acquires first point group information regarding respective distances from a reference position to a plurality of positions measured by the measurement unit; A second acquisition unit configured to acquire map information in which second point cloud information based on a plurality of pieces of position information and a weight value based on the reliability of the second point cloud information are recorded for each area; A calculation unit that calculates an evaluation value for each area based on a result of matching of the area based on the weighting value of the point cloud information and the second point cloud information , and a total that is a sum of the calculated evaluation values. A position estimating unit for estimating the reference position based on a parameter having the highest evaluation indicated by the evaluation value .

The invention according to claim 11, which is a control method executed by the information processing device, wherein the first acquisition unit acquires first point group information regarding each distance from the reference position to a plurality of positions measured by the measurement unit. A second obtaining step of obtaining map information in which a step , second point group information based on one or a plurality of pieces of position information, and a weight value based on the reliability of the second point group information are recorded for each area. A calculating step of calculating an evaluation value for each area based on a result of matching of the first point group information and the second point group information for each area based on the weighting value; and the calculated evaluation value. And a position estimating step of estimating the reference position based on the parameter having the highest evaluation indicated by the total evaluation value that is the sum of

The invention according to claim 12 is a program executed by a computer, wherein the first acquisition unit acquires first point group information regarding each distance from a reference position to a plurality of positions measured by a measurement unit; A second acquisition unit configured to acquire map information in which second point cloud information based on one or a plurality of pieces of position information and a weight value based on the reliability of the second point cloud information are recorded for each area; A calculating unit configured to calculate an evaluation value for each area based on a result of matching of the first point group information and the second point group information for each area based on the weighting value, and a sum of the calculated evaluation values The computer is caused to function as a position estimating unit that estimates the reference position based on a parameter having the highest evaluation indicated by a certain overall evaluation value .

Claims (14)

A first acquisition unit that acquires first point group information regarding each distance from the reference position to the plurality of positions measured by the measurement unit,
A second acquisition unit that acquires map information in which second point group information based on one or more pieces of position information is recorded for each area;
A calculation unit that calculates an evaluation value for each of the areas based on a result of the comparison of the first point group information and the second point group information for each area;
An information processing apparatus, comprising: an extraction unit that extracts, from the area in which the evaluation value is calculated, an area in which the evaluation value is lower than a predetermined threshold.   The information processing apparatus according to claim 1, wherein the calculation unit normalizes the evaluation value for each area based on the number of points for each area acquired as the first point group information. Further comprising a position estimating unit for estimating the reference position,
The map information includes a weight value based on the reliability of the second point group information for each of the regions,
The information processing apparatus according to claim 1, wherein the position estimating unit estimates the reference position based on a comparison result for each area based on a weight value for each area.   The position estimating unit estimates the reference position based on a parameter having the highest evaluation indicated by an overall evaluation value that is a sum of evaluation values calculated for each of the regions from the matching result using the weighting value. Item 4. The information processing device according to item 3. The map information includes information on the number of points for each area of the second point cloud information,
The said extraction part determines the area | region to extract based on the information of the number of points for every area | region contained in the said map information, and the number of points for every area | region acquired as said 1st point group information. Item 5. The information processing device according to any one of Items 1 to 4.   The transmitting device according to claim 1, further comprising a transmitting unit configured to transmit region information on the region extracted by the extracting unit to a server device that manages map information in which second point group information is recorded for each region. An information processing device according to claim 1.   The information processing apparatus according to claim 6, wherein the transmission unit transmits, to the server device, area information on an area where a predetermined number or more of the areas extracted by the extraction unit are adjacent to each other.   The server selectively selects one of region information on all regions extracted by the extraction unit and region information on regions where a predetermined number or more of the regions extracted by the extraction unit are adjacent to each other. The information processing apparatus according to claim 6, wherein the information is transmitted to the apparatus. A receiving unit that receives area information from the plurality of information processing apparatuses according to claim 6,
Based on a plurality of area information for each area received by the receiving unit, a determination unit that determines, for each area, whether or not the area where a static object change has occurred,
Server device comprising:   The transmission unit according to claim 9, further comprising: a transmission unit configured to transmit, to the information processing apparatus, a transmission request of measurement data by the measurement unit with respect to the region determined by the determination unit to be a region where a static object change has occurred. Server device. Further comprising a storage unit for storing a weighting value for matching for each region,
The server device according to claim 9, wherein a weight value of the area is set lower for an area in which the receiving unit receives the area information by a predetermined number or more. A control method executed by the information processing device,
A first acquisition step of acquiring first point cloud information on each distance from the reference position to the plurality of positions measured by the measurement unit,
A second acquisition step of acquiring map information in which second point group information based on one or a plurality of pieces of position information is recorded for each area;
A calculating step of calculating an evaluation value for each area based on a result of matching of the first point cloud information and the second point cloud information for each area;
An extraction step of extracting an area in which the evaluation value is lower than a predetermined threshold value from among the areas in which the evaluation value has been calculated. A program executed by a computer,
A first acquisition unit that acquires first point group information regarding each distance from the reference position to the plurality of positions measured by the measurement unit,
A second acquisition unit that acquires map information in which second point group information based on one or more pieces of position information is recorded for each area;
A calculation unit that calculates an evaluation value for each of the areas based on a result of the comparison of the first point group information and the second point group information for each area;
A program that causes the computer to function as an extraction unit that extracts, from the area in which the evaluation value is calculated, an area in which the evaluation value is lower than a predetermined threshold.   A storage medium storing the program according to claim 13.

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