WO2019188745A1

WO2019188745A1 - Information processing device, control method, program, and storage medium

Info

Publication number: WO2019188745A1
Application number: PCT/JP2019/011974
Authority: WO
Inventors: 加藤　正浩
Original assignee: パイオニア株式会社
Priority date: 2018-03-28
Filing date: 2019-03-22
Publication date: 2019-10-03

Abstract

An on-vehicle machine 1 acquires a measurement value Z_i _, by each one of at least three LIDARs 2 provided to a vehicle, to a landmark included in the respective measurement ranges of the LIDARs 2. The on-vehicle machine 1 acquires a predicted measurement value Z^- _i predicted on the basis of landmark coordinates indicated by position information of the landmark registered in the map DB 10. The on-vehicle machine 1 detects at least one LIDAR 2, in which misalignment has occurred with respect to the mounting position on the vehicle, on the basis of a plurality of difference values ΔZ_i between the measurement value Z_i acquired for each one of the LIDARs 2 and the predicted measurement value Z^- _i.

Description

Information processing apparatus, control method, program, and storage medium

The present invention relates to a technique for detecting a positional deviation of a measurement unit.

2. Description of the Related Art Conventionally, a technique for estimating a vehicle position based on measurement data of a measurement unit such as a radar or a camera is known. For example, Patent Document 1 discloses a technique for estimating a self-position by collating the output of a measurement sensor with the position information of a feature registered in advance on a map. Patent Document 2 discloses a vehicle position estimation technique using a Kalman filter.

JP 2013-257742 A JP 2017-72422 A

Data obtained from measurement units such as radar and cameras are coordinate system values based on the measurement unit, and are data that depends on the attitude of the measurement unit with respect to the vehicle. Need to be converted to Therefore, when a deviation occurs in the posture of the measurement unit, there is a possibility that an error occurs in the measurement value after conversion into the coordinate system based on the vehicle.

The present invention has been made to solve the above-described problems, and it is an object of the present invention to provide an information processing apparatus that can suitably detect a positional deviation of a measuring unit that measures a distance to an object with respect to a moving body. With a purpose.

The invention according to the claim is an information processing apparatus, and obtains a measurement distance obtained by the measurement unit up to an object included in a measurement range of each of at least three measurement units provided in the moving body. A predicted distance acquisition unit that acquires a predicted distance from the moving body to the target predicted based on position information of the target, the measured distance acquired for each of the measuring units, and the predicted distance A detection unit configured to detect, from the at least three measurement units, at least one measurement unit in which a displacement of the attachment position with respect to the moving body has occurred based on a plurality of difference values.

The invention described in the claims is a control method executed by the information processing apparatus, wherein the measurement unit measures up to an object included in a measurement range of each of at least three measurement units provided in the moving body. A measurement distance acquisition step of acquiring a distance; a predicted distance acquisition step of acquiring a predicted distance from the moving body predicted based on position information of the target object to the target object; And a detection step of detecting at least one measurement unit in which the displacement of the attachment position with respect to the moving body is generated from the at least three measurement units based on a plurality of difference values between the measurement distance and the predicted distance.

The invention described in the claims is a program executed by a computer, and obtains a measurement distance by the measurement unit to an object included in each measurement range of at least three measurement units provided in the moving body. A measurement distance acquisition unit, a prediction distance acquisition unit that acquires a prediction distance from the moving object predicted based on position information of the object, and the measurement distance acquired for each measurement unit. Based on a plurality of difference values of the predicted distance, the computer is caused to function as a detection unit that detects from at least three measurement units at least one measurement unit in which a displacement of the attachment position with respect to the moving body has occurred.

It is a schematic block diagram of a driving assistance system. It is a block diagram which shows the functional structure of a vehicle equipment. It is the figure which represented the state variable vector by the two-dimensional orthogonal coordinate. It is a figure which shows the schematic relationship between a prediction step and a measurement update step. An example of the functional block of a control part is shown. It is a graph which shows the time change of the difference value for every rider in predetermined time. It is an example of the flowchart which shows the execution procedure of the estimation process of the position and attitude | position based on the detection process of a shift | offset | difference lidar, and the 1st estimation method. It is an example of the flowchart which shows the execution procedure of the detection process of a shift | offset | difference generation | occurrence | production lidar, and the estimation process of the position and attitude | position based on a 2nd estimation method. It is a figure which shows the relationship between the vehicle coordinate system represented by the two-dimensional coordinate, and a lidar coordinate system. It is a figure which shows the relationship between the vehicle coordinate system represented by the three-dimensional coordinate, and a lidar coordinate system.

According to a preferred embodiment of the present invention, the information processing apparatus acquires a measurement distance by the measurement unit to an object included in each measurement range of at least three measurement units provided in the moving body. An acquisition unit; a predicted distance acquisition unit that acquires a predicted distance from the moving object to the target predicted based on position information of the target; and the measured distance and the predicted distance acquired for each of the measuring units And a detection unit that detects, from the at least three measurement units, at least one measurement unit in which the displacement of the attachment position with respect to the moving body is generated based on the plurality of difference values. Here, the “positional deviation” is not limited to the deviation of the center of gravity position of the measuring unit with respect to the moving body, but also includes a deviation of the orientation (posture) that does not involve the deviation of the center of gravity position. According to this aspect, the information processing apparatus can suitably detect a measurement unit in which a positional deviation has occurred by comparing difference values calculated from measurement distances of three or more measurement units.

In one aspect of the information processing apparatus, the detection unit detects, from the at least three measurement units, measurement units in which a positional deviation has occurred based on an average value of the difference values for each measurement unit in a predetermined time. According to this aspect, the information processing apparatus can accurately detect a measurement unit in which a difference value difference is steadily generated as a measurement unit in which a position error occurs.

In another aspect of the information processing apparatus, the information processing apparatus includes a predicted position acquisition unit that acquires the predicted predicted position of the moving body for each measurement unit, and a value obtained by multiplying the difference value by a predetermined gain. And a correction unit that corrects the predicted position for each measurement unit, and the predicted position acquisition unit corrects the predicted position corrected based on the difference value corresponding to one measurement unit of the measurement unit. Then, the predicted position corresponding to the measurement unit other than the measurement unit is acquired. According to this aspect, the information processing apparatus can perform highly accurate position estimation using the measurement distances of the three measurement units.

In another aspect of the information processing apparatus, the correction unit corrects the predicted position based on the difference value of a measurement unit other than the measurement unit detected by the detection unit. According to this aspect, it is possible to suitably suppress a decrease in position estimation accuracy caused by using the measurement result of the measurement unit in which the positional deviation occurs.

In another aspect of the information processing apparatus, the predicted distance acquisition unit corrects the predicted position corrected based on the difference value of a measurement unit other than the measurement unit detected by the detection unit, and the measurement detected by the detection unit. The information processing device acquires the predicted distance based on the position information of the target object measured by the unit, and the information processing apparatus detects the detection unit based on the predicted distance and the measured distance detected by the measurement unit. An estimation unit for estimating the position of the measurement unit is provided. Here, the “position” estimated by the estimation unit is not limited to the position of the center of gravity of the measurement unit, and may include the direction (posture) of the measurement unit. According to this aspect, the information processing apparatus can detect the predicted distance of the target object based on the predicted position of the moving object and the position of the target object on the map predicted by the measurement unit in which the positional error has not occurred, and the measurement in which the positional error has occurred. It is possible to suitably estimate the position (including the posture) of the measurement unit where the positional deviation has occurred so that the measurement distance of the object by the unit matches or approximates.

In another aspect of the information processing apparatus, the information processing apparatus includes: the predicted position corrected based on the difference value of the measurement unit detected by the detection unit; and a measurement unit other than the measurement unit detected by the detection unit. An estimation unit configured to estimate the position of the measurement unit detected by the detection unit based on the predicted position corrected based on the difference value; Also according to this aspect, the information processing apparatus can suitably estimate the position (including the posture) of the measurement unit where the positional deviation has occurred.

In another aspect of the information processing apparatus, the estimation unit estimates the displacement amount of the positional deviation based on the estimated position of the measurement unit and the position of the measurement unit stored in the storage unit. According to this aspect, the information processing apparatus can perform processing such as correction of the measurement result of the measurement unit in which the positional deviation has occurred, and can appropriately suppress a decrease in accuracy of processing using the measurement unit. In a preferred example, the estimation unit may detect, as the deviation amount, a deviation amount in the pitch direction, yaw direction, or roll direction of the measurement unit detected by the detection unit, or a three-dimensional measurement unit detected by the detection unit. It is preferable to calculate at least one of the shift amounts of the center of gravity position in the space.

According to another preferred embodiment of the present invention, there is provided a control method executed by the information processing apparatus, wherein the measurement up to an object included in each measurement range of at least three measurement units provided in the moving body is performed. A measurement distance acquisition step of acquiring a measurement distance by a unit, a predicted distance acquisition step of acquiring a predicted distance from the moving body to the target predicted based on position information of the target, and acquisition for each measurement unit A detection step of detecting, from the at least three measurement units, at least one measurement unit in which a displacement of the attachment position with respect to the moving body has occurred based on a plurality of difference values between the measured distance and the predicted distance, Have By using this control method, the information processing apparatus can suitably detect the measurement unit in which the positional deviation has occurred.

According to another preferred embodiment of the present invention, the program executed by the computer is measured by the measurement unit up to an object included in each measurement range of at least three measurement units provided on the moving body. A measurement distance acquisition unit that acquires a distance; a predicted distance acquisition unit that acquires a predicted distance from the moving object to the target predicted based on position information of the target; and the measurement acquired for each measurement unit Based on a plurality of difference values between the measurement distance and the predicted distance, the computer functions as a detection unit that detects from at least three measurement units at least one measurement unit in which the displacement of the attachment position with respect to the moving body has occurred. Let By executing this program, the computer can suitably detect the measurement unit in which the positional deviation has occurred. Preferably, the program is stored in a storage medium.

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

[Schematic configuration]
FIG. 1A is a schematic configuration diagram of a driving support system according to the present embodiment. The driving support system shown in FIG. 1 (A) is mounted on a vehicle and has an in-vehicle device 1 that performs control related to driving support of the vehicle, a lidar (Lider: Light Detection and Ranging, or Laser Illuminated Detection And Ranging) 2 (2A 2C), a gyro sensor 3, a vehicle speed sensor 4, and a GPS receiver 5. FIG. 1B is an overhead view of the vehicle showing an example of the arrangement of the lidar 2.

The in-vehicle device 1 is electrically connected to the rider 2, the gyro sensor 3, the vehicle speed sensor 4, and the GPS receiver 5, and based on these outputs, the position of the vehicle on which the in-vehicle device 1 is mounted ("own vehicle position"). Also called.) 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 vehicle-mounted device 1 stores a map database (DB: DataBase) 10 that stores road data and feature information that is information on landmarks (landmarks) provided in the vicinity of the road. The above-mentioned landmark features are, for example, features such as kilometer posts, 100-meter posts, delineators, traffic infrastructure facilities (eg signs, direction signs, signals), utility poles, street lights, etc. that are periodically lined up on the roadside. The feature information is information in which at least an index assigned to each feature, position information of the feature, and information on the direction of the feature are associated with each other. And the vehicle equipment 1 estimates the own vehicle position by making it collate with the output of the lidar 2 etc. based on this feature information.

Further, the in-vehicle device 1 detects a rider 2 (also referred to as a “deviation-generating lidar”) in which a positional deviation (including an attitude deviation) has occurred based on the vehicle position estimation result by each rider 2 and the occurrence of the deviation. Estimate the position and attitude angle of the rider. And the vehicle equipment 1 performs the process etc. which correct | amend each measured value of the point cloud data which a shift | offset | difference generation | occurrence | production lidar outputs based on this estimation result. The in-vehicle device 1 is an example of the “information processing device” in the present invention.

The lidar 2 (2A to 2C) emits a pulse laser to a predetermined angle range in the horizontal direction and the vertical direction, thereby discretely measuring the distance to the object existing in the outside world, and determining the position of the object. The three-dimensional point cloud information shown is generated. In this case, the lidar 2 includes 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 scan data based on 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 light received by the light receiving unit and the distance to the object in the irradiation direction of the laser light specified based on the light reception signal, and is supplied to the in-vehicle device 1. The In this embodiment, as shown in FIG. 1B, the

riders

2A and 2B are provided at the front portion of the vehicle, and the rider 2C is provided at the rear portion of the vehicle. The lidar 2 is an example of the “measurement unit” in the present invention. Thus, at least three riders 2 according to the present embodiment are provided. The rider 2, the gyro sensor 3, the vehicle speed sensor 4, and the GPS receiver 5 each supply output data to the in-vehicle device 1.

FIG. 2 is a block diagram showing a functional configuration of the in-vehicle device 1. The in-vehicle device 1 mainly includes an interface 11, a storage unit 12, an input unit 14, a control unit 15, and an information output unit 16. Each of these elements is connected to each other via a bus line.

The interface 11 acquires output data from sensors such as the lidar 2, the gyro sensor 3, the vehicle speed sensor 4, and the GPS receiver 5, and supplies the output data to the control unit 15. In addition, the interface 11 supplies a signal related to the traveling control of the vehicle generated by the control unit 15 to an electronic control unit (ECU: Electronic Control Unit) of 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 includes a map DB 10 and rider installation information IL. The lidar installation information IL is information related to the relative three-dimensional position and posture angle of each rider 2 measured at a certain reference time (for example, when there is no positional deviation such as immediately after alignment adjustment of the rider 2). In the present embodiment, the attitude angle of the lidar 2 and the like is represented by a roll angle, a pitch angle, and a yaw angle (that is, Euler angle).

The input unit 14 is a button operated by the user, a touch panel, a remote controller, a voice input device, and the like, and receives an input for specifying a destination for route search, an input for specifying on / off of automatic driving, and the like. . The information output unit 16 is, for example, a display or a speaker that outputs 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. The control unit 15 estimates the vehicle position based on the output signal of each sensor supplied from the interface 11 and the map DB 10, and controls vehicle driving support including automatic driving control based on the estimation result of the vehicle position. And so on. At this time, when the output data of the rider 2 is used, the control unit 15 uses the measurement data output by the rider 2 as the reference for the attitude angle and position of the rider 2 recorded in the rider installation information IL. The reference coordinate system (also referred to as “rider coordinate system”) is converted into a coordinate system based on the vehicle (also referred to as “vehicle coordinate system”). Further, in the present embodiment, the control unit 15 detects the lidar 2 in which the positional deviation has occurred, and estimates the position and posture angle of the lidar 2. The control unit 15 includes a “measurement distance acquisition unit”, a “predicted distance acquisition unit”, a “detection unit”, a “predicted position acquisition unit”, a “correction unit”, an “estimation unit”, and a “computer” that executes a program. Is an example.

[Outline of the vehicle position estimation process]
First, the outline | summary of the estimation process of the own vehicle position by the control part 15 is demonstrated.

The control unit 15 outputs the gyro sensor 3, the vehicle speed sensor 4, and / or the GPS receiver 5 based on the measured values of the distance and angle by the lidar 2 with respect to the landmark and the position information of the landmark extracted from the map DB 10. The vehicle position estimated from the data is corrected. In the present embodiment, as an example, the control unit 15 performs a prediction step of predicting the vehicle position from output data of the gyro sensor 3 and the vehicle speed sensor 4 based on a state estimation method based on Bayesian estimation, and a previous prediction step. A measurement update step for correcting the calculated predicted value of the vehicle position is executed alternately. Various filters developed to perform Bayesian estimation can be used as the state estimation filter used in these steps, and examples thereof include an extended Kalman filter, an unscented Kalman filter, and a particle filter. As described above, various methods have been proposed for position estimation based on Bayesian estimation.

In the following, the vehicle position estimation using the extended Kalman filter will be briefly described.

FIG. 3 is a diagram showing the state variable vector x in two-dimensional orthogonal coordinates. As shown in FIG. 3, the vehicle position on the plane defined on the two-dimensional orthogonal coordinates of xy is represented by coordinates “(x, y)” and the direction (yaw angle) “ψ” of the vehicle. . Here, the yaw angle ψ is defined as an angle formed by the traveling direction of the vehicle and the x-axis. The description here is based on four variables (x, y, z, ψ) taking into account the coordinates of the z axis perpendicular to the x axis and the y axis in addition to the coordinates (x, y) and the yaw angle ψ described above. The vehicle position is estimated using the state variable of the vehicle position. This is because a general road has a gentle slope, so it is possible to ignore the pitch angle and roll angle of the vehicle. However, six variables including the pitch angle and roll angle of the vehicle are not used. An example of the vehicle position estimation using the vehicle position state variable will also be described later.

FIG. 4 is a diagram illustrating a schematic relationship between the prediction step and the measurement update step. FIG. 5 shows an example of functional blocks of the control unit 15. As shown in FIG. 4, by repeating the prediction step and the measurement update step, calculation and update of the estimated value of the state variable vector “X” indicating the vehicle position are sequentially executed. Moreover, as shown in FIG. 5, the control part 15 has the position estimation part 21 which performs a prediction step, and the position estimation part 22 which performs a measurement update step. The position prediction unit 21 includes a dead reckoning block 23 and a position prediction block 24, and the position estimation unit 22 includes a landmark search / extraction block 25 and a position correction block 26. In FIG. 4, the state variable vector of the reference time (ie, current time) “k” to be calculated is expressed as “X ⁻ (k)” or “X ^{^} (k)” (“state variable Vector X (k) = (denoted as x (k), y (k), z (k), ψ (k)) ^T ”). Here, the provisional estimated value (predicted value) estimated in the predicting step is appended with “ ^- ” on the character representing the predicted value, and the estimated value with higher accuracy updated in the measurement updating step. Is appended with “ ^{^} ” on the character representing the value.

In the prediction step, the dead reckoning block 23 of the control unit 15 moves the vehicle moving speed “v” and the angular velocity “ω” (collectively, “control value u (k) = (v (k), ω (k))”. ^T ”is used to obtain the movement distance and azimuth change from the previous time. The position prediction block 24 of the control unit 15 adds the obtained moving distance and azimuth change to the state variable vector X ^{^} (k-1) at the time k-1 calculated in the immediately previous measurement update step, so that the time k A predicted value (also referred to as “predicted position”) X ⁻ (k) is calculated. At the same time, the covariance matrix “P ⁻ (k)” corresponding to the error distribution at the predicted position X ⁻ (k) is changed to the covariance matrix “at time k−1 calculated in the immediately preceding measurement update step”. P ^{^} (k-1) ".

In the measurement update step, the landmark search / extraction block 25 of the control unit 15 associates the landmark position vector registered in the map DB 10 with the scan data of the lidar 2. Then, the landmark search / extraction block 25 of the control unit 15, when this association is possible, the measurement value “Z (k)” by the lidar 2 of the landmark that has been associated, and the predicted position X ⁻ ( k) and a landmark measurement value obtained by modeling the measurement processing by the lidar 2 using the landmark position vector registered in the map DB 10 (referred to as “measurement prediction value”) “Z ⁻ (k)”. And get respectively. The measured value Z (k) is a vector value in the vehicle coordinate system converted from the landmark distance and scan angle measured by the lidar 2 at time k into components with the vehicle traveling direction and the lateral direction as axes. Then, the position correction block 26 of the control unit 15 calculates a difference value between the measured value Z (k) and the measured predicted value Z ⁻ (k).

The position correction block 26 of the control unit 15 then adds the Kalman gain “K (k) to the difference value between the measured value Z (k) and the measured predicted value Z ⁻ (k) as shown in the following equation (1). ”And adding this to the predicted position X ⁻ (k), an updated state variable vector (also referred to as“ estimated position ”) X ^{^} (k) is calculated.

Further, in the measurement update step, the position correction block 26 of the control unit 15 is similar to the prediction step in that the covariance matrix P ^{^} (k) (simply referred to as P (k) corresponding to the error distribution of the estimated position X ^{^} (k). Is expressed from the covariance matrix P ⁻ (k). Parameters such as the Kalman gain K (k) can be calculated in the same manner as a known self-position estimation technique using an extended Kalman filter, for example.

In this way, the prediction step and the measurement update step are repeatedly performed, and the predicted position X ⁻ (k) and the estimated position X ^{^} (k) are sequentially calculated, so that the most likely vehicle position is calculated. .

Next, the vehicle position estimation using a plurality of riders 2 will be described. In this embodiment, when no displacement occurs in any of the riders 2, the vehicle position is determined by using the Kalman filter update equation corresponding to equation (1) using the measured values of the riders 2A to 2C. Estimate with high accuracy. In this case, the landmarks detected by the lidars 2A to 2C may be the same or different.

Here, the measured values for the landmark at time “k” in the point cloud data of the riders 2A to 2C are “Z ₁ (k)”, “Z ₂ (k)”, and “Z ₃ (k)”, respectively. The measurement predicted values corresponding to these are assumed to be “Z ^- ₁ (k)”, “Z ^- ₂ (k)”, and “Z ^- ₃ (k)”. In this case, the Kalman filter update formulas for the riders 2A to 2C are expressed by the following formulas (2-1) to (2-3).

In this embodiment, the control unit 15 uses the estimated position X ^{^} (k) obtained by using the equation (2-1) based on the measured value Z ₁ (k) of the lidar 2A as the predicted position of the lidar 2B. X ⁻ (k) is substituted, and the estimated position X ^{^} (k) is calculated from the equation (2-2) based on the measured value Z ₂ (k) of the lidar 2B. Further, the control unit 15 substitutes this estimated position X ^{^} (k) as the predicted position X ⁻ (k) of the lidar 2C, and from the equation (2-3) based on the measured value Z ₃ (k) of the lidar 2C. Estimated position X ^{^} (k) is calculated. In this way, it is possible to calculate the estimated position X ^{^} (k) with high accuracy using the measurement values of the riders 2A to 2C.

The predicted measurement value Z ⁻ _i (k) (i = 1, 2, 3) is expressed by the following equation (3).

Here, “x _mi (j)”, “y _mi (j)”, and “z _mi (j)” are also called landmark positions (“landmark coordinates”) of index number j extracted from the map DB 10. ). Here, since the measured predicted value is represented by a vehicle coordinate system with the traveling direction of the vehicle as an axis, the landmark coordinates are represented by predicted positions x ⁻ (k), y ⁻ (k), z ⁻ (k ) And a rotation matrix using the vehicle yaw angle ψ, it is converted into a vehicle coordinate system. Hereinafter, directions along the respective coordinate axes in the vehicle coordinate system (vehicle traveling direction, left-right direction, and height direction) are also simply referred to as “x direction”, “y direction”, and “z direction”, respectively.

Further, the measured predicted value Z ⁻ _i (k) (= [x ⁻ _Li (k), y ⁻ _Li (k), z ⁻ _Li (k)] ^T ) and the measured value Z _i (k) (= [x _Li (K), y _Li (k), z _Li (k)] ^T ) and the difference value “ΔZ _i (k)” (= [Δx _Li (k), Δy _Li (k), Δz _Li (k)] ^T ) is represented by the following formula (4).

This difference value ΔZ _i (k) is used for detection of a deviation occurrence lidar, as will be described later.

[Detection of misaligned lidar]
The control unit 15 calculates an average value for a predetermined time of a difference value between the measured predicted value Z ⁻ _i (k) and the measured value Z _i (k) of each rider 2, and the average value of the rider whose average value is separated from 0 2 is detected as a deviation occurrence lidar. For example, the predetermined time is set to a time length such that the difference value ΔZ calculated based on the measurement value of the rider 2 in which no positional deviation has occurred is substantially zero.

FIG. 6A is a graph showing temporal changes in the coordinate values Δx _L1 , Δy _L1 , and Δz _L1 of the difference value ΔZ ₁ (k) of the rider 2A at a predetermined time. FIG. 6B is a graph showing temporal changes in the coordinate values Δx _L2 , Δy _L2 , and Δz _L2 of the difference value ΔZ ₂ (k) of the rider 2B at a predetermined time. FIG. 6C is a graph showing temporal changes in the coordinate values Δx _L3 , Δy _L3 , and Δz _L3 of the difference value Z ₃ (k) of the rider 2C at a predetermined time. In the examples of FIGS. 6A to 6C, it is assumed that only the lidar 2C has a positional shift.

In this case, as shown in FIG. 6A, the time average size of the coordinate values Δx _L1 , Δy _L1 , Δz _L1 of the difference value ΔZ ₁ (k) in a predetermined time is close to zero. Similarly, the magnitude of the time average of the coordinate values Δx _L2 , Δy _L2 , Δz _L2 of the difference value ΔZ ₂ (k) shown in FIG. On the other hand, a displacement occurs in the lidar 2C, and the measurement value of the lidar 2C in the z coordinate is affected by the displacement. Therefore, in this case, as shown in FIG. 6C, although the coordinate values Δx _L3 and Δy _L3 of the difference value ΔZ ₃ (k) in the predetermined time are close to 0, the coordinate value The magnitude of the time average of Δz _{L3 at} a predetermined time is clearly different from zero.

As described above, the difference value ΔZ based on the measurement value of the lidar 2 in which the positional deviation has occurred is an abnormal value if at least one of the coordinate values x _L , y _L , and z _L of the measured value Z is caused by the positional deviation. As a result, at least one of the coordinate values Δx _L , Δy _L , Δz _L of the difference value ΔZ is far from 0.

Considering the above, in the present embodiment, the control unit 15 calculates the time average of the coordinate values Δx _L , Δy _L , Δz _L of the difference values ΔZ of the riders 2 for a predetermined time, and the magnitude of the time average When there is a coordinate value of the difference value ΔZ that becomes larger than a predetermined value, the corresponding rider 2 is regarded as a position shift occurrence rider. As a result, it is possible to detect the position shift occurrence lidar with high accuracy. Note that the control unit 15 determines that the position deviation occurrence rider cannot be specified when the time average size of the coordinate values of the difference values ΔZ corresponding to all the riders 2 is larger than a predetermined value, and at least any of them is determined. The information output unit 16 may output a warning or the like indicating that a positional deviation has occurred in the rider 2.

[Estimation of posture angle and position]
Next, an estimation method (first estimation method and second estimation method) of the attitude angle and position of the deviation-generating lidar will be described. Hereinafter, as an example, a method for estimating the position and posture angle of the rider 2C when the rider 2C is used as a displacement-generating rider will be described.

(1) First Estimation Method In the first estimation method, the control unit 15 uses the vehicle position estimation result estimated by the rider 2 other than the deviation occurrence rider and the landmark coordinates of the landmark measured by the deviation occurrence rider. The measured predicted value of the landmark calculated based on the calculated value is calculated as an expected value (also referred to as “measured expected value”) of the measured value of the deviation occurrence lidar 2. Then, the control unit 15 performs the least square method using the Newton method based on the set of the measurement expected value and the actual measurement value of the deviation occurrence lidar, and estimates the attitude angle and position of the deviation occurrence lidar.

Here, positional deviation is measured predicted value Z rider 2C by not not rider 2A and rider 2B which is a deviation occurs rider based on the vehicle position estimation result of the estimation occurs ^- When measured expected value _{3 (k),} measuring expected value _{_{[x b (k), y}} b (k), z b (k)] T is expressed by the following equation (5).

Further, the measurement predicted value ^Z rider 2C is a deviation occurs rider _- 3 (k) is expressed by the following equation (6).

Here, each predicted value x ⁻ , y ⁻ , z ⁻ , ψ ⁻ is estimated based on the measured values of the

lidars

2A and 2B in which no positional deviation occurs, as shown in the following equation (7). Indicates the position.

In this way, the expected measurement value [x _b (k), y _b (k), z _b (k)] ^T is the landmark coordinate of the landmark of the index number j measured by the lidar 2C, which is the displacement-generating lidar [ x _m3 (j), y _m3 (j), z _m3 (j)] ^T is the vehicle position estimation result (x ⁻ , y ⁻ , z ⁻ , ψ ⁻ ) by the

riders

2A and 2B in which no positional deviation has occurred. Is the value converted into the vehicle coordinate system. Then, the control unit 15 performs the least square method using the Newton method on the measurement expected value shown in the equation (5), and the roll angle “L _φ ” and the pitch angle “ L _θ ”, yaw angle“ L _ψ ”, position“ L _x ”in the x direction, position“ L _y ”in the _y direction, and position“ L _z ”in the z direction are estimated. The relationship between the vehicle coordinate system and the lidar coordinate system is determined by these posture angles L _φ , L _θ , L _ψ and positions L _x , L _y , L _z . The relationship between the vehicle coordinate system and the lidar coordinate system will be described in detail in the section “(3) Coordinate system conversion”.

Next, a specific example of a method for calculating the attitude angles L _φ , L _θ , L _ψ and the positions L _x , L _y , L _z of the deviation-generating lidar by the least square method using the Newton method will be described. Hereinafter, the measured value of the landmark at time k by the shift generation lidar is expressed as [x _L (k), y _L (k), z _L (k)] ^T.

In this case, the measured values [x _L (k), y _L (k), z _L (k)] ^T use posture angles L _φ , L _θ , L _ψ , and positions L _x , L _y , L _z . Is represented by the following equation (8).

The following formula (9) is an equivalent formula to the formula (8).

Generally, in order to obtain six variables (attitude angles L _φ , L _θ , L _ψ , and positions L _x , L _y , L _z ), at least six equations are required. Therefore, the solution of these six variables is obtained by substituting into the formula (9) using the measurement result and the expected measurement value at least twice. In addition, since the accuracy of the solution by the least square method can be improved as the number of measured values and measured expected values to be used is increased, preferably, all obtained measured values and measured expected values are used. Good.

Further, since the functions of x _L (k), y _L (k), and z _L (k) shown in Expression (9) are nonlinear functions, a solution cannot be obtained analytically. Therefore, in this embodiment, the solution is linearized around the initial value and the solution is obtained by sequential calculation using the Newton method. Therefore, the following formula (10) is created.

Here, the partial differential expression with respect to x _L (k) is expressed by the following expression (11).

Moreover, the equation of partial differentiation with respect to _yL (k) is expressed by the following equation (12).

Furthermore, the equation of partial differentiation with respect to z _L (k) is represented by the following equation (13).

Here, initial values of posture angles and positions stored in the rider installation information IL (that is, no deviation occurs) are “L _φ0 ”, “L _θ0 ”, “L _ψ0 ”, “L _x0 ”, _{Assuming that} “L _y0 ” and “L _z0 ”, the following equation (14) similar to the equation (9) is established.

Then, taking as an example a case where a solution is obtained based on three measurement values, a set of measurement values and measurement expected values for three times {[x _L1 (k), y _L1 (k), z _L1 (k) ] ^T , [ _xb1 (k), _yb1 (k), _zb1 (k)] ^T }, {[ _xL2 (k), _yL2 (k), _zL2 (k)] ^T , [ _xb2 (K), y _b2 (k), z _b2 (k)] ^T }, {[x _L3 (k), y _L3 (k), z _L3 (k)] ^T , [x _b3 (k), y _b3 When (k), z _b3 (k)] ^T } is used, a linear form as shown in the following equation (15) is established.

Hereinafter, in Equation (15), the 9-row and 1-column matrix (that is, the left side) of the measurement values for 3 times is “z”, the 9-by-6 Jacobian matrix is “C”, and the initial values of the posture angle and position The matrix of 6 rows and 1 column indicating the difference between the current value and the current value is “Δx”, and the matrix of 9 rows and 1 column regarding the initial measurement values x _L0 , y _L0 , and z _L0 obtained by the equation (14) is “d”. In this case, Expression (15) is expressed by the following expression.
z = CΔx + d

Here, when an error “v” exists between z calculated by the linearized expression “z = CΔx + d” and a measured value “z _ob ” actually measured,
z _ob = z + v
Is established. Therefore,
v = −CΔx−d + z _ob
Is established.

Here, if Δz = z _ob −d, the square sum of errors “v ^T v” is expressed by the following equation (16).

Therefore, Δx when the right side of Equation (16) is the minimum is the desired solution.

Here, when the square sum of errors “v ^T v” is minimized, since the partial differential is 0, the relationship shown in the following equation (17) is established.

And based on Formula (17), the following Formula (18) is materialized.

Then, when both sides of the equation (18) are transposed, the following equation (19) corresponding to the normal equation is obtained.

Therefore, Δx is expressed by the following equation (20).

Then, by substituting Δx obtained by Expression (20) into an expression “x = Δx + x ₀ ”, a least square solution is obtained. Repeat recurrence formula calculation of the x as a new x _0, if the change in x is minimal, it is the position and orientation of the final misalignment occurs rider.

Taking the above into consideration, the control unit 15 can suitably estimate the attitude angle and position of the deviation-generating lidar by performing the following first to fifth steps.

First, in the first step, the control unit 15 includes initial positions L _x0 , L _y0 , L _z0 and attitude angles L _φ0 , L _θ0 , L _ψ0 stored in the rider installation information IL, and a plurality of measurement expected values. Based on, the Jacobian matrix C and the initial measurement values [x _L0 , y _L0 , z _L0 ] ^T based on the equation (14) are calculated. Then, in the second step, the control unit 15 generates a Gyorei "z _ob" described above from a plurality of measurements, subtracting the matrix above constructed from the initial measured value "d", Delta] z (= z _ob -d) is calculated.

Next, in the third step, the control unit 15 calculates Δx based on Expression (20). In the fourth step, the control unit 15 obtains a least squares solution based on the expression “x = Δx + x ₀ ”. In the fifth step, if Δx is larger than the predetermined value, the control unit 15 sets x to the positions L _x0 , L _y0 , L _z0 and the posture angles L _φ0 , L _θ0 , L _ψ0 , and the first step again. Return to. On the other hand, when Δx is equal to or smaller than a predetermined value, the control unit 15 regards x obtained in the fourth step as a final solution, and positions L _x , L _y , L _z indicated by _x and the posture angle L _φ. , L _θ , L _ψ are regarded as the position and posture angle of the displacement-generating lidar.

FIG. 7 is an example of a flowchart showing the execution procedure of the position / posture angle estimation process based on the detection process of the deviation occurrence lidar and the first estimation method. The control unit 15 repeatedly executes the process of the flowchart of FIG.

First, the control unit 15 performs vehicle position estimation using a Kalman filter using three or more riders (in this case, the riders 2A to 2C) while the vehicle is traveling (step S101). In this case, the control unit 15 calculates the estimated position X ^{^} reflecting the measurement value of each rider 2 by sequentially executing the Kalman filter update formulas shown in Formulas (2-1) to (2-3). To do. In this case, the control unit 15, as described above, the estimated position X ^{^} based on the measurement values of one rider 2 predicted position of the other rider 2 X ^- is substituted as.

Next, the control unit 15 averages the difference value ΔZ _i between the measured predicted value Z ⁻ _{i of} each rider 2 and the measured value Z _i for a predetermined time until the current processing reference time (step S102). . In this case, the control unit 15 stores the past difference value ΔZ _i for the predetermined time in a buffer such as the storage unit 12, and each coordinate value Δx _{Li of the} stored difference value ΔZ _i for the past predetermined time. , Δy _Li , Δz _Li are averaged.

Then, the control unit 15 determines whether there is a rider 2 whose average difference value ΔZ _i is away from 0 (step S103). For example, the control unit 15 determines whether or not the absolute value of any of the coordinate values Δx _Li , Δy _Li , Δz _Li of the averaged difference value ΔZ _i is equal to or greater than a predetermined value. The above-mentioned predetermined value is a value larger than the average absolute value of Δx _Li , Δy _Li , Δz _Li based on the measured value of the lidar 2 in which no deviation occurs, taking into account the length of the predetermined time used for averaging, etc. Is set to be Then, when there is a rider 2 whose average difference value ΔZ _i is away from 0 (step S103; Yes), the control unit 15 shifts the rider 2 corresponding to the difference value ΔZ _i whose average is away from 0. And the own vehicle position estimation process is executed by the rider 2 other than the deviation occurrence rider (step S104). On the other hand, when there is no rider 2 whose average difference value ΔZ _i is away from 0 (step S103; No), the control unit 15 regards that there is no deviation occurrence rider and returns the process to step S101 again.

In step S104, after executing the own vehicle position estimation process by the rider 2 other than the deviation occurrence rider, the control unit 15 performs measurement prediction from the own vehicle position estimation result and the landmark coordinates of the landmark registered in the map DB 10. A value Z ⁻ is calculated and set as a measurement expected value [x _b (k), y _b (k), z _b (k)] ^T of the deviation generation lidar (step S105). The control unit 15 selects a landmark that can be measured by the deviation generation lidar as the above-described landmark. Then, the control unit 15 acquires a plurality of sets of measurement values and measurement expectation values of the deviation occurrence lidar for the above-described landmarks.

Next, the control unit 15 performs a least square method using the Newton method based on a plurality of sets of measurement expected values and measurement values, and estimates the attitude angle and position of the deviation-generating lidar (step S106). The use of the posture angle and position of the estimated deviation generation lidar will be described in the section “(3) Application Example”.

(2) Second Estimation Method In the second estimation method, the control unit 15 uses the own vehicle position estimated by the rider 2 other than the deviation occurrence rider (also referred to as “reference position”) and the own vehicle estimated by the deviation occurrence rider. Based on the difference value with the position (also referred to as “temporary position”), the position and the posture angle of the deviation occurrence lidar are updated. Hereinafter, the state variables of the vehicle position estimated by the Kalman filter are assumed to be six variables of position x, y, z, yaw angle ψ, roll angle φ, and pitch angle θ.

First, the measured predicted value Z ⁻ _i (k) for the landmark at time k of each rider 2 is expressed by the following equation (21).

Then, the control unit 15 detects the deviation occurrence lidar based on the average of the difference values ΔZ _i (k) as described in [Detection of deviation occurrence lidar]. Here, it is assumed that the control unit 15 detects the lidar 2C as a shift generation lidar. Then, the control unit 15 uses the reference positions [x ^{^} _r (k), y ^{^} _r (k), z ^{^} _r (k), φ ^{^} _r (k), estimated by the

lidars

2A and 2B other than the deviation-generating lidar. θ ^{^} _r (k), ψ ^{^} _r (k)] ^T and the temporary position [x ^{^} ₃ (k), y ^{^} ₃ (k), z ^{^} ₃ (k), φ ^{^} ₃ (k), θ ^{^} ₃ (k), ψ ^{^} ₃ (k)] ^T are calculated, and the difference values [Δx, Δy, Δz, Δφ, Δθ, Δψ] ^T are calculated by the following formula ( 22).

And the control part 15 adds the difference value shown by Formula (22) to the position and attitude | position angle recorded on lidar installation information IL, or these update values, and is the present position and attitude | position of a shift | offset | difference lidar Update corners. Thereafter, when the above-described difference value is sufficiently close to 0 (for example, the absolute value of each element Δx, Δy, Δz, Δφ, Δθ, Δψ of the difference value is not more than a predetermined value), the control unit 15 When the update of the posture angle is completed and the difference value is not close to 0, a new difference value is calculated again by calculating the reference position and the temporary position, and the position and posture angle are updated.

FIG. 8 is an example of a flowchart showing the execution procedure of the position angle and position estimation processing based on the detection processing of the deviation occurrence lidar and the second estimation method. The control unit 15 repeatedly executes the process of the flowchart of FIG.

First, the control unit 15 performs vehicle position estimation using a Kalman filter using three or more riders (here, riders 2A to 2C) while the vehicle is traveling (step S201). Next, the control unit 15, the measurement predicted value of each rider 2 Z ^- averaging at a given time with respect to the difference values [Delta] Z _i of _i and the measured value Z _i, until the current standards (step S202).

Then, when there is a rider 2 whose average difference value ΔZ _i is away from 0 (step S203; Yes), the control unit 15 shifts the rider 2 corresponding to the difference value ΔZ _i whose average is away from 0. And the own vehicle position estimated from the measured values of the lidar 2 other than the deviation-generating lidar is set as a reference position (step S204). Further, the control unit 15 sets the own vehicle position estimated from the measurement value of the deviation occurrence lidar as a temporary position (step S205). Then, the control unit 15 updates the estimated value of the position and posture angle of the deviation-generating lidar based on the difference value between the reference position and the temporary position (step S206). Then, when the difference value between the reference position and the temporary position is close to 0 (step S207; Yes), the control unit 15 determines that the estimated value of the position and posture angle of the deviation occurrence lidar is sufficiently accurate. Then, the process of the flowchart ends. On the other hand, when the difference value between the reference position and the temporary position is not close to 0 (step S207; No), the control unit 15 returns the process to step S204, and recalculates the reference position.

(3) Conversion of Coordinate System FIG. 9 is a diagram showing the relationship between the vehicle coordinate system and the lidar coordinate system represented by two-dimensional coordinates. Here, the vehicle coordinate system has a coordinate center “x _v ” along the traveling direction of the vehicle and a coordinate axis “y _v ” along the lateral direction of the vehicle with the vehicle center as the origin. The lidar coordinate system has a coordinate axis “x _L ” along the front direction of the rider 2 (see arrow A <b> 2) and a coordinate axis “y _L ” along the side surface direction of the rider 2.

Here, when the yaw angle of the lidar 2 with respect to the vehicle coordinate system is “L _ψ ” and the position of the lidar 2 is [L _x , L _y ] ^T , the measurement point [x] at the time “k” viewed from the vehicle coordinate system [x _v (k), y _v (k)] ^T is converted to the coordinates [x _L (k), y _L (k)] ^T of the lidar coordinate system by the following equation (23) using the rotation matrix “C _ψ ”. Converted.

On the other hand, the transformation from the lidar coordinate system to the vehicle coordinate system may be performed using an inverse matrix (transpose matrix) of the rotation matrix. Therefore, the measurement point [x _L (k), y _L (k)] ^T obtained at the time k obtained in the lidar coordinate system is expressed by the coordinates [x _v (k), y _v of the vehicle coordinate system according to the following equation (24). (K)] It is possible to convert to ^T.

FIG. 10 is a diagram illustrating the relationship between the vehicle coordinate system and the lidar coordinate system represented by three-dimensional coordinates. Here, a coordinate axis perpendicular to the coordinate axes x _v and y _v is “z _v ”, and a coordinate axis perpendicular to the coordinate axes x _L and y _L is “z _L ”.

The roll angle of the lidar 2 with respect to the vehicle coordinate system is “L _φ ”, the pitch angle is “L _θ ”, the yaw angle is “L _ψ ”, the position of the lidar 2 on the coordinate axis x _v is “L _x ”, and the coordinate axis y _v When the position is “L _y ” and the position on the coordinate axis z _v is “L _z ”, the measurement point [x _v (k), y _v (k), z _v ( k)] ^T is the following equation (25) using the direction cosine matrix “C” represented by the rotation matrices “C _φ ”, “C _θ ”, and “C _ψ ” corresponding to roll, pitch, and yaw. Is converted into coordinates [x _L (k), y _L (k), z _L (k)] ^T in the lidar coordinate system.

On the other hand, the transformation from the lidar coordinate system to the vehicle coordinate system may be performed using an inverse matrix (transpose matrix) of the direction cosine matrix. Therefore, the measurement point [x _L (k), y _L (k), z _L (k)] ^T obtained at the time k acquired in the lidar coordinate system is expressed by the coordinate [x _v (K), y _v (k), z _v (k)] ^T.

(4) Application Example Here, a specific example of the use of the estimated position and posture angle of the displacement generation lidar estimated by the first estimation method or the second estimation method will be described.

For example, the control unit 15 calculates the estimated posture angle and position change amount of the position deviation occurrence lidar with respect to the posture angle and position of the position deviation occurrence rider before the occurrence of the deviation recorded in the rider installation information IL, and Based on the amount of change, each measurement value of the point cloud data output by the misregistration occurrence lidar is corrected. In this case, for example, the control unit 15 stores a map or the like indicating the correction amount of the measurement value for each change amount, and corrects the above-described measurement value by referring to the map or the like. Alternatively, the measurement value may be corrected using the value of a predetermined ratio of the change amount as the correction amount of the measurement value. The control unit 15 stops the use of the position shift occurrence lidar and outputs a predetermined warning by the information output unit 16 when detecting a shift in position or posture angle at which the change amount is equal to or greater than a predetermined threshold. May be.

In another example, the control unit 15 uses the calculated roll angle L _φ , pitch angle L _θ , yaw angle L _ψ , x-direction position L _x , y-direction position L _y , and z-direction position L _z to use the lidar 2. Each measured value of the point cloud data output from the vehicle may be converted from the lidar coordinate system to the vehicle body coordinate system, and based on the converted data, the vehicle position estimation or automatic driving control may be executed. Accordingly, the measurement value of the rider 2 can be appropriately converted into the vehicle coordinate system based on the attitude angle and position of the rider 2 after the occurrence of the deviation.

In yet another example, when the in-vehicle device 1 includes an adjustment mechanism such as an actuator for correcting the posture angle and position of each rider 2, the posture angle of the rider 2 based on the estimation result. And the position may be modified. In this case, the in-vehicle device 1 calculates the estimated amount of change in posture angle and position on the basis of the posture angle and position recorded in the rider installation information IL, and the posture angle and position of the rider 2 by the amount of the change amount. Control is performed to drive the adjustment mechanism so as to correct.

As described above, the in-vehicle device 1 according to the present embodiment acquires the measurement value Z _i by the rider 2 up to the landmark included in each measurement range of at least three riders 2 provided in the vehicle. Then, the in-vehicle device 1 acquires a predicted measurement value Z ^- _i predicted based on the landmark coordinates indicated by the position information of the landmark registered in the map DB 10. The in-vehicle device 1 has at least one displacement of the mounting position with respect to the vehicle based on a plurality of difference values ΔZ _i between the measured value Z _i and the measured predicted value Z ⁻ _i acquired for each rider 2. The lidar 2 is detected. As a result, the in-vehicle device 1 can accurately detect the lidar 2 in which the positional deviation has occurred.

[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 control unit 15 may individually execute the Kalman filter update formula shown in Formula (1) based on the measurement value of each rider 2. That is, in this case, the control unit 15 performs vehicle position estimation based on the measurement value of the lidar 2A, vehicle position estimation based on the measurement value of the lidar 2B, and vehicle position estimation based on the measurement value of the lidar 2C based on the formula (1). And the difference value ΔZ between the measured value Z calculated at the time of executing each formula (1) and the measured predicted value Z ⁻ is compared. Then, when there is a difference value ΔZ that is greater than or equal to a predetermined value, the control unit 15 detects the rider 2 corresponding to the difference value ΔZ as a deviation occurrence rider. In this case, as in the embodiment, the landmarks to be measured by the riders 2 do not have to be the same.

(Modification 2)
The configuration of the driving support system shown in FIG. 1 is an example, and the configuration of the driving support system to which the present invention is applicable is not limited to the configuration shown in FIG. For example, in the driving support system, instead of having the in-vehicle device 1, the electronic control device of the vehicle may execute the processing shown in FIG. In this case, the lidar installation information IL is stored in, for example, a storage unit in the vehicle, and the vehicle electronic control device is configured to be able to receive output data of various sensors such as the lidar 2.

DESCRIPTION OF SYMBOLS 1 In-vehicle apparatus 2 Rider 3 Gyro sensor 4 Vehicle speed sensor 5 GPS receiver 10 Map DB

Claims

A measurement distance acquisition unit for acquiring a measurement distance by the measurement unit to an object included in each measurement range of at least three measurement units provided in the moving body;
A predicted distance acquisition unit that acquires a predicted distance from the moving object to the target predicted based on position information of the target;
Based on a plurality of difference values between the measurement distance and the predicted distance acquired for each measurement unit, at least one measurement unit in which the displacement of the attachment position with respect to the moving body is generated from the at least three measurement units. A detection unit to detect;
An information processing apparatus.
The information processing apparatus according to claim 1, wherein the detection unit detects, from the at least three measurement units, a measurement unit in which a positional deviation has occurred based on an average value of the difference values for each measurement unit over a predetermined time. .
A predicted position acquisition unit that acquires the predicted position of the predicted moving body for each measurement unit;
A correction unit that corrects the predicted position for each measurement unit according to a value obtained by multiplying the difference value by a predetermined gain; and
The said predicted position acquisition part acquires the said predicted position corrected based on the said difference value corresponding to one measurement part of the said measurement part as a predicted position corresponding to measurement parts other than the said measurement part. The information processing apparatus described in 1.
The information processing apparatus according to claim 3, wherein the correction unit corrects the predicted position based on the difference value of a measurement unit other than the measurement unit detected by the detection unit.
The predicted distance acquisition unit includes the predicted position corrected based on the difference value of the measurement unit other than the measurement unit detected by the detection unit, and the position information of the object measured by the measurement unit detected by the detection unit. Obtaining the predicted distance based on
The estimation unit according to any one of claims 1 to 4, further comprising: an estimation unit that estimates a position of the measurement unit detected by the detection unit based on the predicted distance and a measurement distance by the measurement unit detected by the detection unit. Information processing device.
The detection based on the predicted position corrected based on the difference value of the measurement unit detected by the detection unit and the predicted position corrected based on the difference value of a measurement unit other than the measurement unit detected by the detection unit. The information processing apparatus according to any one of claims 1 to 4, further comprising an estimation unit that estimates a position of the measurement unit detected by the unit.
The information processing apparatus according to claim 5, wherein the estimation unit estimates the displacement amount of the positional deviation based on the estimated position of the measurement unit and the position of the measurement unit stored in the storage unit.
The estimation unit, as the amount of displacement, is the amount of displacement in the pitch direction, yaw direction, or roll direction of the measurement unit detected by the detection unit, or the barycentric position in the three-dimensional space of the measurement unit detected by the detection unit The information processing apparatus according to claim 7, wherein at least one of the shift amounts is calculated.
A control method executed by the information processing apparatus,
A measurement distance acquisition step of acquiring a measurement distance by the measurement unit to an object included in each measurement range of at least three measurement units provided on the moving body;
A predicted distance acquisition step of acquiring a predicted distance from the moving object predicted based on position information of the target object to the target object;
Based on a plurality of difference values between the measurement distance and the predicted distance acquired for each measurement unit, at least one measurement unit in which the displacement of the attachment position with respect to the moving body is generated from the at least three measurement units. A detection process to detect;
A control method.
A program executed by a computer,
A measurement distance acquisition unit for acquiring a measurement distance by the measurement unit to an object included in each measurement range of at least three measurement units provided in the moving body;
A predicted distance acquisition unit that acquires a predicted distance from the moving object to the target predicted based on position information of the target;
Based on a plurality of difference values between the measurement distance and the predicted distance acquired for each measurement unit, at least one measurement unit in which the displacement of the attachment position with respect to the moving body is generated from the at least three measurement units. A program for causing the computer to function as a detection unit for detection.
A storage medium storing the program according to claim 10.