Information processing device, information processing method, and information processing program

Abstract

An information processing device according to the present disclosure comprises a movement control unit that moves a moving body, an acquisition unit that acquires a plurality of pieces of detection information in which the same detection target is detected by each of a plurality of detection units provided to the moving body while moving the moving body by using the movement control unit, and a setting unit that sets a parameter of the detection unit on the basis of the detection information acquired by the acquisition unit.

Description

Information processing device, information processing method, and information processing program

[0001] The present disclosure relates to an information processing device, an information processing method, and an information processing program.

[0002] A mobile body such as a robot may be provided with a detection unit. For example, a legged robot having legs may be provided with a distance measurement sensor such as a LiDAR (Light Detection and Ranging) sensor that can detect targets such as objects at high density.

[0003] However, errors occur between the measured values ​​of parameters related to the position and orientation of the detector and the design values ​​due to assembly errors, vibration, aging, etc. This error has a significant impact on the entire device, such as a moving object. For this reason, there is a demand for technologies to accurately set parameters, such as calibration technology to reduce errors.

[0004] A known calibration technique involves extracting specific reference points from detection information obtained by a detection unit installed in a vehicle, and setting parameters for the detection unit based on the detection target detected from the reference points.

[0005] Japanese Patent Application Laid-Open No. 2023-4964

[0006] In conventional techniques, calibration can usually be performed by removing noise based on feature points of the detection target, correcting distortion, optimizing parameters based on multiple pieces of detection information, and so on.

[0007] However, when a detection unit is provided on a moving body as in the prior art, calibration may not be possible without the use of special components. For example, when a legged robot is provided with a detection unit such as LiDAR with a narrow FOV (Field of View), the detection unit faces the ground, and the detection ranges are unlikely to overlap, making it impossible to perform calibration based on the detection target. This is because, unlike when a detection unit is provided on an arm whose orientation is variable, the detection unit faces the ground and can only detect the ground and objects on it, which have few feature points, and the detection ranges usually do not overlap, making it impossible to detect the same detection target.

[0008] Therefore, the present disclosure proposes an information processing device, an information processing method, and an information processing program that can perform calibration without using special components even when a detection unit is provided on a moving object.

[0009] In order to solve the above problems, one form of information processing device according to the present disclosure includes a movement control unit that moves a moving body, an acquisition unit that acquires multiple pieces of detection information in which the same detection target is detected by multiple detection units provided on the moving body while the moving body is moved by the movement control unit, and a setting unit that sets parameters of the detection units based on the detection information acquired by the acquisition unit.

[0010] FIG. 1 is a diagram (1) showing an overview of an information processing system according to an embodiment. FIG. 2 is a diagram (2) showing an overview of an information processing system according to an embodiment. FIG. 3 is a block diagram showing an example of a configuration of an information processing device according to an embodiment. FIG. 4 is a diagram for explaining a setting process according to an embodiment. FIG. 5 is a diagram for explaining motion distortion. FIG. 6 is a diagram for explaining motion distortion correction based on motion information. FIG. 1 is a diagram showing an example of a setting process according to an embodiment. FIG. 2 is a diagram showing an example of a setting process according to an embodiment. FIG. 3 is a diagram showing an example of a setting process according to an embodiment. FIG. 4 is a diagram showing an example of a setting process according to an embodiment. FIG. 5 is a flowchart showing an example of a procedure of information processing according to an embodiment. FIG. 6 is a hardware configuration diagram showing an example of a computer that realizes the functions of an information processing device according to the present disclosure.

[0011] Hereinafter, embodiments will be described in detail with reference to the drawings. In the following embodiments, the same components are designated by the same reference numerals, and redundant description will be omitted.

[0012] The present disclosure will be described in the following order: 1. Embodiment 1-1. Overview of information processing system according to embodiment 1-2. Configuration of information processing device according to embodiment 1-3. Specific example of setting process according to embodiment 1-4. Procedure of information processing according to embodiment 1-5. Modification according to embodiment 2. Other embodiments 3. Effects of information processing device according to the present disclosure 4. Hardware configuration

[0013] (1. Embodiment) (1-1. Overview of Information Processing System According to Embodiment) An overview of the information processing system according to the embodiment will be described using the example of FIG. 1. FIG. 1 is a diagram (1) showing an overview of the information processing system according to the embodiment. The information processing system is composed of an information processing device 100, a mobile object 200, a terminal 300, and a user 400.

[0014] The information processing device 100 is integrated with a mobile object 200, such as an autonomously moving legged robot, and is a control device such as a computer that controls the movement of the mobile object 200. The mobile object 200 includes a plurality of detection units 210, such as LiDAR, distance measurement sensors such as a depth camera, or a stereo camera, that scan a detection target and acquire detection information such as point cloud data.

[0015] The detection units 210 are provided one each on the front and rear or left and right sides of the moving body 200. This is to ensure that the moving body 200 has a wide overall detection range even if the detection range, such as the FOV, of each detection unit 210 is narrow. The detection units 210 are provided facing downward so that the ground or floor surface is included in the detection range of each detection unit 210. Furthermore, the detection units 210 are fixed to the moving body 200.

[0016] The detection units 210 are provided at positions or in orientations such that the detection ranges of the respective detection units 210 do not overlap when the moving body 200 is stationary. Specifically, the first detection unit 211 and the second detection unit 212 are provided at positions such that the detection range 501 of the first detection unit 211 and the detection range 502 of the second detection unit 212 do not overlap when the moving body 200 is stationary.

[0017] The terminal 300 is a PC (Personal Computer) or the like that displays provided information, a UI (User Interface), etc. on a display unit such as a monitor. The terminal 300 exchanges information with the information processing device 100 and presents the provided information to the user 400. The provided information is information about at least one of the parameter setting process and result, and is expressed as text information for informing the user 400 whether there are any problems with the parameter setting process or result.

[0018] The user 400 is a manager or operator of the mobile object 200. The user 400 inputs command information relating to a command to the information processing device 100 into the terminal 300, and receives provision information from the information processing device 100 via the terminal 300.

[0019] As described above, a mobile body 200 such as a legged robot is provided with a distance measurement sensor such as LiDAR as the detection unit 210, which is capable of detecting targets such as objects at high density. However, errors occur between the measured values ​​of parameters related to the position and orientation of the detection unit 210 and their design values ​​due to assembly errors, vibrations, aging, etc. This error has a significant impact on the entire device such as the mobile body 200. For this reason, there is a demand for techniques for accurately setting parameters, such as calibration techniques for reducing errors.

[0020] However, when the moving body 200 is provided with a detection unit 210 such as a LiDAR with a narrow FOV, the detection unit 210 faces the ground and the detection ranges of the detection units 210 are unlikely to overlap, making it difficult to perform calibration. In particular, when the detection unit 210 is provided at a position where the detection range 501 of the first detection unit 211 and the detection range 502 of the second detection unit 212 do not overlap when the moving body 200 is stationary, as shown in FIG. 1 , and the detection unit 210 is fixed to the moving body 200, it is difficult to perform calibration based on the detection target.

[0021] This is because, unlike when the detection unit is mounted on an arm whose orientation can be changed, the detection unit 210 faces the ground, so it can only detect the ground and objects on it, which have few feature points, and the detection ranges usually do not overlap, so it is not possible to detect the same detection target.

[0022] Therefore, the information processing device 100 according to the present disclosure executes the following information processing with the aim of performing calibration without using special components even when the detection unit 210 is provided in the moving object 200. The information processing device 100 causes each of the multiple detection units 210 provided in the moving object 200 to detect the same detection target while moving the moving object 200. The information processing device 100 sets parameters for the detection units 210 based on the multiple pieces of detection information detected by the respective detection units 210, and provides provided information to the terminal 300 and the user 400.

[0023] An example of the above-mentioned information processing will be described below with reference to Fig. 1. The user 400 inputs command information to the terminal 300 to move the moving object 200 by a predetermined distance at a predetermined speed. The terminal 300 transmits the command information to the information processing device 100.

[0024] The information processing device 100 moves the moving object 200 based on command information received from the terminal 300. The information processing device 100 acquires a plurality of pieces of detection information while moving the moving object 200, and sets parameters for the detection unit 210 based on the detection information. The information processing device 100 provides the provided information to the terminal 300 and the user 400.

[0025] This point will be described in detail using the example of Fig. 2. Fig. 2 is a diagram (2) showing an overview of the information processing system according to the embodiment. The moving body 200 includes a moving unit 220, which is a leg unit, and is provided on the ground or floor surface around the first object 601 and the second object 602.

[0026] The information processing device 100 causes the moving body 200 to rotate around the location or move around the first object 601, which is the same detection target, so that the first object 601 is included in the detection range 501 of the first detection unit 211 and the detection range 502 of the second detection unit 212.

[0027] The information processing device 100 causes the first detection unit 211 and the second detection unit 212 to preferentially detect a detection target in which errors are easy to detect, such as a box-shaped object including multiple flat surfaces, such as a cardboard box.

[0028] By having the first detection unit 211 and the second detection unit 212 detect such objects as detection targets with priority, the information processing device 100 can more easily detect that an error has occurred in the vertical direction than when a cylindrical object is detected.

[0029] Next, while moving the mobile object 200, the information processing device 100 acquires first detection information obtained by scanning the first object 601 by the first detection unit 211 and second detection information obtained by scanning the first object 601 by the second detection unit 212. The information processing device 100 acquires, as these pieces of detection information, a plurality of point cloud data each including feature points of the first object 601 that is the detection target.

[0030] The information processing device 100 further acquires movement information related to the movement of the moving object 200, such as at least one of the position, movement trajectory, and movement amount of the moving object 200.

[0031] The information processing device 100 acquires, as the position of the moving body 200, its own position estimated by scan matching, SLAM (Simultaneous Localization and Mapping), IMU (Inertial Measurement Unit), amount of movement, and the like.

[0032] The information processing device 100 acquires the position and inclination of the moving body 200 at each time as a movement trajectory or movement amount based on the movement amount and speed of the moving body 200 indicated by command information or an estimated self-position. The information processing device 100 may acquire the position and inclination of the moving body 200 at each time as a movement trajectory or as a movement amount. The information processing device 100 can also acquire the amount of movement of the moving body 200 from a certain time to another time as a movement amount, calculated based on the position and inclination of the moving body 200 at each time.

[0033] The information processing device 100 performs calibration based on detection information accumulated as the moving body 200 moves, and sets parameters for the first detection unit 211 and the second detection unit 212. At that time, the information processing device 100 performs at least one of noise removal to remove noise from the detection information, parameter optimization based on the detection information, and motion distortion correction to correct motion distortion caused by the movement of the moving body 200.

[0034] The information processing device 100 sets, as parameters, parameters related to the position or orientation of each of the detection units 210. The information processing device 100 sets, as parameters, parameters for performing coordinate conversion of each point of the point cloud data between a coordinate system based on the position or orientation of each of the detection units 210 and a world coordinate system.

[0035] An example in which the information processing device 100 performs noise removal, motion distortion correction, and optimization in this order will be described below.

[0036] The information processing device 100 removes flying pixels from at least one of the plurality of pieces of detection information based on feature points to remove noise. The information processing device 100 removes feature points of the first object 601 that do not match between the first detection information and the second detection information as flying pixels from the detection information. The information processing device 100 is not limited to an algorithm based on feature points, and can use any noise removal algorithm.

[0037] Next, the information processing device 100 divides the point cloud data into a plurality of plane point cloud data in which the same planes are detected by the respective detection units 210, and a plurality of object point cloud data in which the same objects are detected by the respective detection units 210. The information processing device 100 performs the division by detecting the plane point cloud data and the object point cloud data from the point cloud data using a plane detection algorithm and an object detection algorithm. The information processing device 100 can employ any plane detection algorithm and object detection algorithm.

[0038] Next, the information processing device 100 performs motion distortion correction on the point cloud data based on the motion information. For example, when the moving object 200 turns around the spot, the information processing device 100 performs motion distortion correction on the point cloud data based on the motion information.

[0039] The information processing device 100 performs motion distortion correction based on at least one of the position, the movement trajectory, and the movement amount of the moving object 200 as the motion information.

[0040] For example, the information processing device 100 calculates distortion coefficients based on at least one of the position, movement trajectory, and movement amount of the moving object 200. Then, as movement distortion correction, the information processing device 100 calculates a vector that is the product of the vector of coordinates of each point of the plane point cloud data and the object point cloud data included in the first detection information and the second detection information, respectively, and a matrix of distortion coefficients. The information processing device 100 sets this vector to the coordinates of each point and performs coordinate transformation of each point.

[0041] Next, the information processing device 100 performs error minimization to minimize errors between the point cloud data as optimization based on the detection information including the point cloud data and the movement information. The information processing device 100 minimizes errors between the planar point cloud data and errors between the object point cloud data, and sets parameters to minimize errors between the point cloud data as optimization.

[0042] For example, the information processing device 100 sets parameters for the first detection unit 211 and the second detection unit 212 so that the sum of the error between the planar point cloud data and the error between the object point cloud data is minimized. The information processing device 100 provisionally sets parameters related to the position or orientation of each of the first detection unit 211 and the second detection unit 212.

[0043] As an optimization step, the information processing device 100 further sets at least one of the movement amount and movement trajectory of the moving object 200 that minimizes the error between the point cloud data.

[0044] Next, the information processing device 100 determines whether the setting process for the parameter setting has been completed. If the error between the pieces of detected information is greater than a preset allowable error value, the information processing device 100 determines that the setting process has not been completed, and transmits to the terminal 300 provision information 700 relating to the parameter setting process, including the convergence rate of the error and promotion information.

[0045] The tolerance is set in advance by command information issued by the information processing device 100 or the user 400. The error convergence rate is information obtained by dividing the tolerance by the error between the pieces of detected information, and is expressed as text information such as "Current: XX% completed" in Fig. 2 to inform the user 400 how much the mobile object 200 needs to be moved before the error converges.

[0046] The promotion information is information that encourages at least one of the movement of the moving body 200 and the detection of a new detection target, and is information that advises the user 400 on how to move the moving body 200 and what kind of detection target to detect in order to reduce errors.

[0047] The prompting information is expressed as text information such as "Please scan the object. Please scan the object with the second ranging sensor" in Figure 2, which prompts the mobile body 200 to move further and scan the second object 602 with the second detection unit 212.

[0048] If the information processing device 100 determines that the error will converge to a value equal to or less than the allowable value, it transmits to the terminal 300 prompting information to encourage the terminal 300 to make the error smaller, as shown in Fig. 2. If the information processing device 100 determines that the error will not converge, it transmits to the terminal 300 prompting information to reset the movement of the moving body 200 and the detection of the detection target up to that point, and to start over from the beginning.

[0049] The terminal 300 receives the provided information 700 from the information processing device 100 and displays it on a monitor. Based on the provided information 700, the user 400 inputs command information to the terminal 300 to instruct the mobile object 200 to further move and scan the second object 602 with the second detection unit 212. The terminal 300 transmits this command information to the information processing device 100.

[0050] The information processing device 100 repeats the above-described information processing, and when the error between the pieces of detected information converges to an allowable value or less, it determines that the setting processing is completed and transmits provided information regarding the results of the parameter setting to the terminal 300. The terminal 300 receives this provided information from the information processing device 100 and displays it on a monitor.

[0051] If the user 400 finds no problem with the parameter setting results indicated by the provided information regarding the parameter setting results, the user 400 inputs command information to the terminal 300 instructing the parameter to be officially set. The terminal 300 transmits this command information to the information processing device 100. The information processing device 100 officially sets the parameter based on this command information.

[0052] In this way, the information processing device 100 moves multiple detection units 210 so that their detection ranges overlap as the moving body 200 moves, causing the detection units 210 to detect the same detection target, and sets the parameters of the detection units 210 based on the multiple detection information.

[0053] As a result, even if the number of feature points of the detection target is small, the information processing device 100 can remove noise by matching feature points of the same detection target in the detection information. Furthermore, the information processing device 100 can perform coordinate transformation using movement distortion correction on each point of multiple point cloud data in which the same detection target is detected, and set parameters to minimize the error between the point cloud data.

[0054] Therefore, the information processing device 100 can perform calibration without using any special components even when multiple detection units 210 are provided on the moving object 200. In other words, the information processing device 100 can take advantage of the advantage of calibration based on feature points and movement information, that is, calibration can be performed without using any special components, and can make the drawback of being dependent on the number of feature points less noticeable.

[0055] Furthermore, unlike manual or visual calibration, which requires millimeter-level adjustments and data confirmation, and calibration that requires special components, the information processing device 100 can guarantee a predetermined accuracy similar to these calibrations without the drawbacks of these calibrations.

[0056] Furthermore, by performing motion distortion correction, the information processing device 100 can perform calibration that is particularly effective for a moving body 200 such as a legged robot, which is prone to shaking three-dimensionally and generating motion distortion even when simply turning in place.

[0057] (1-2. Configuration of information processing device according to embodiment) Next, the configuration of the information processing device 100 according to the embodiment will be described using the example of FIG. 3. FIG. 3 is a block diagram showing an example of the configuration of the information processing device according to the embodiment. The information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0058] The communication unit 110 is a network interface card (NIC), a network interface controller, etc. The communication unit 110 is connected to a network via a wired or wireless connection, and transmits and receives information to and from the terminal 300 via the network.

[0059] The storage unit 120 is a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk, etc. The storage unit 120 stores various types of information such as a plurality of pieces of detection information and parameters.

[0060] The control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing a program stored in the storage unit 120 using RAM as a work area. The control unit 130 may also be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 130 controls the information processing device 100. The control unit 130 includes a movement control unit 131, an acquisition unit 132, a setting unit 133, and a provision unit 134.

[0061] The movement control unit 131 moves the moving body 200. The movement control unit 131 moves the moving unit 220 of the moving body 200 by a predetermined movement amount at a predetermined speed indicated by the command information. The movement control unit 131 also moves the moving body 200 so that the same detection target is included in the detection range of each of the detection units 210.

[0062] The movement control unit 131 first causes the moving body 200 to turn around the spot. If the detection unit 210 does not detect the detection target, the movement control unit 131 causes the moving body 200 to move around the detection target. The movement control unit 131 can cause the moving body 200 to perform any movement such as linear movement, circular movement, or movement in a figure-eight pattern.

[0063] The acquisition unit 132 acquires a plurality of pieces of detection information while moving the moving object 200 using the movement control unit 131. The acquisition unit 132 acquires a plurality of point cloud data including feature points of the same detection target detected by each of the detection units 210 as the detection information.

[0064] The acquisition unit 132 further acquires movement information, which includes at least one of the position, the movement trajectory, and the movement amount of the moving object 200.

[0065] For example, the acquisition unit 132 acquires, as the position of the moving body 200, a self-position estimated by SLAM, IMU, the amount of movement of the moving body 200, etc. The acquisition unit 132 acquires, as the movement trajectory and amount of movement, the position and inclination of the moving body 200 at each time based on the amount of movement and speed indicated by the command information and the estimated self-position.

[0066] The setting unit 133 sets parameters for the detection units 210 based on the detection information acquired by the acquisition unit 132. The setting unit 133 performs at least one of noise removal, optimization, and motion distortion correction, and sets parameters related to the position or orientation of each of the detection units 210 as parameters.

[0067] The setting process will be described using the example of Fig. 4. Fig. 4 is a diagram for explaining the setting process according to the embodiment. In Fig. 4, noise removal, motion distortion correction, and optimization are performed in this order.

[0068] In step S1, the setting unit 133 removes noise by removing flying pixels based on the feature points acquired by the acquisition unit 132. The setting unit 133 removes, from the detection information, feature points of the first object 601 that do not match between the first detection information and the second detection information as flying pixels.

[0069] In step S2, the setting unit 133 performs plane detection using a plane detection algorithm to detect plane point cloud data from the point cloud data included in each of the first detection information and the second detection information. In step S3, the setting unit 133 performs object detection using an object detection algorithm to detect object point cloud data from the point cloud data included in each of the first detection information and the second detection information. The plane point cloud data is N １ , N ２ ...The object point cloud data is expressed as O １ ｉ , O ２ ｉ The i indicates the number of the object.

[0070] In step S4, the movement control unit 131 and the setting unit 133 perform movement distortion correction based on the movement information acquired by the acquisition unit 132. For example, when the movement control unit 131 causes the moving object 200 to turn around on the spot, the setting unit 133 performs movement distortion correction.

[0071] An example of motion distortion correction will be described using Figures 5 and 6. Figure 5 is a diagram for explaining motion distortion. Figure 6 is a diagram for explaining motion distortion correction based on motion information. Here, the detection unit 210 is a LiDAR, and typically outputs a detection target scanned within 100 ms as one frame of point cloud data.

[0072] As shown in FIG. 5, when the moving body 200 is stationary, the position of the LiDAR observation point at the start of the scan is the same as the position of the LiDAR observation point at the end of the scan.

[0073] 5, when the moving object 200 moves, the absolute position of the LiDAR changes during the scan, and the position of the LiDAR observation point at the start of the scan is no longer the same as the position of the LiDAR observation point at the end of the scan. As a result, the coordinate system of the observation point is distorted, causing motion distortion. Therefore, the setting unit 133 performs motion distortion correction based on at least one of the position, motion trajectory, and motion amount of the moving object 200 acquired by the acquisition unit 132.

[0074] In FIG. 6, the setting unit 133 performs motion distortion correction for motion distortion occurring within one scan based on the self-position, which is the position of the moving object 200, using a backpropagation algorithm.

[0075] The setting unit 133 sets the self-position “x ｊ , u ｊ , 0” and the time interval “Δt” of each LiDAR observation point in one scan, the weight is set to “−Δtf(x ｊ , u ｊ , 0) is calculated. ｊ , u ｊ , 0) is a function depending on the self-position.

[0076] Next, as a translation distortion correction, the setting unit 133 calculates the vector “Xj” of each coordinate of each point corresponding to each observation point of the LiDAR in the plane point cloud data and the object point cloud data, and the weight matrix “−Δtf(x ｊ , u ｊ , 0)) to calculate a vector. The setting unit 133 sets this vector to the coordinates of each point and performs coordinate transformation of each point.

[0077] Returning to the description of FIG. 4, as another example, the setting unit 133 performs motion distortion correction based on the motion trajectory (amount of motion). Hereinafter, the motion trajectory is expressed as the posture x of the moving object 200 at each time, as shown in the following equation (1). ｉ and the posture x of the moving body 200 at each time ｉ However, as shown in the following formula (2), the position t ｉ and slope R ｉ The case where the above is configured will be explained.

[0078]

[0079]

[0080] The setting unit 133 calculates the coordinate vectors of each point at time t in the plane point cloud data and the object point cloud data acquired for each LiDAR scan, and the movement trajectory T ｔ The setting unit 133 sets this vector to the coordinates of each point and performs coordinate transformation of each point.

[0081] The setting unit 133 sets the movement trajectory T ｌ , T ｍ Using the above, the movement trajectory T at time t is calculated as shown in the following equation (3). ｔ Calculate T in the following formula (3): ｄｉｆｆ is expressed as the following formula (4).

[0082]

[0083]

[0084] Next, the setting unit 133 calculates the vector of the coordinates of each point at time t and the movement trajectory T ｔThe setting unit 133 sets this vector to the coordinates of each point and performs coordinate conversion, thereby performing translation distortion correction.

[0085] Next, the setting unit 133 calculates the product of the vector of the coordinates of each point after the motion distortion correction and the matrix of the parameter c related to the position or orientation of each of the first detection unit 211 and the second detection unit 212. The setting unit 133 sets this vector to the coordinates of each point and performs coordinate transformation, thereby converting the coordinate system based on the position or orientation of each of the first detection unit 211 and the second detection unit 212 into a world coordinate system.

[0086] In step S5, the setting unit 133 performs error minimization as optimization based on the multiple pieces of detection information and movement information acquired by the acquisition unit 132. The setting unit 133 minimizes the sum of the error between the planar point cloud data and the error between the object point cloud data. As optimization, the setting unit 133 further sets at least one of the movement amount and movement trajectory of the moving object 200 that minimizes the error between the multiple pieces of point cloud data.

[0087] When the parameters of the first detection unit 211 and the second detection unit 212 are set to have a correct positional relationship, the object point cloud data and the plane point cloud data acquired from these detection units should match completely. Therefore, the error between plane point cloud data and between object point cloud data can be expressed by the following formula (5).

[0088]

[0089] K represents the number of points in the plane point cloud data and the object point cloud data. １ represents the coordinates of each point in the point cloud data included in the first detection information. ２ represents the coordinates of each point in the point cloud data included in the second detection information. || represents the Euclidean distance between the coordinates of two points. || is not limited to the Euclidean distance between the two coordinates, and may be the Mahalanobis distance, the distribution distance, etc.

[0090] The setting unit 133 calculates the error of equation (5) between all of the object point cloud data and between all of the planar point cloud data, and sets the parameters and the movement amount of the moving body 200 so that the total value of the error between the planar point cloud data and the error between the object point cloud data is minimized. The setting unit 133 sets the parameters and the movement amount so that the total value of the error between the planar point cloud data and the error between the object point cloud data, expressed by the following equation (6), is minimized.

[0091]

[0092] Returning to the description of Fig. 3, the providing unit 134 provides the provided information to the terminal 300 and the user 400 via the communication unit 110. The providing unit 134 provides the provided information including a convergence rate of an error obtained by dividing the tolerance by the error between the plurality of pieces of detection information acquired by the acquisition unit 132. The providing unit 134 also provides the provided information including promotion information that prompts at least one of the movement of the mobile object 200 and the detection of a new detection target.

[0093] (1-3. Specific example of setting process according to embodiment) The setting process according to the embodiment is not limited to the example in FIG. 4. An example of the setting process according to the embodiment will be described below with reference to FIG. 7. FIG. 7 is a diagram (1) showing an example of the setting process according to the embodiment. In FIG. 7, the setting process is performed in the order of motion distortion correction and optimization.

[0094] In step S11, the acquisition unit 132 acquires the self-position, which is the position of the moving body 200 at each time estimated by SLAM. In step S12, the setting unit 133 analyzes parameters of the detection unit 210, such as the error between the initial parameter L1 of the first detection unit 211, which is a LiDAR, and the initial parameter L2 of the second detection unit 212, which is also a LiDAR. In step S13, the setting unit 133 performs motion distortion correction in the same manner as in step S4.

[0095] In step S14, the setting unit 133 corrects the parameters related to the position or orientation of the detection unit 210 based on the movement information. The setting unit 133 corrects the parameters by setting the matrix obtained by multiplying a matrix of the movement trajectory of the moving body 200, which is formed from the attitude of the moving body 200 at each time, and a matrix of parameters related to the position or orientation of the second detection unit 212, as the parameters related to the position or orientation of the second detection unit 212 after correction.

[0096] In step S15, the setting unit 133 optimizes the parameters relating to the position or orientation of the detection unit 210 and the movement amount (movement trajectory) of the moving object 200, similarly to step S5.

[0097] Another example of the setting process according to the embodiment will be described with reference to Fig. 8. Fig. 8 is a diagram (2) showing an example of the setting process according to the embodiment. In Fig. 8, the setting process for the first detection unit 211 is performed in the order of noise removal, division, optimization, and motion distortion correction.

[0098] In step S21, the setting unit 133 performs noise removal in the same manner as in step S1. In step S22, the setting unit 133 sets the vector obtained by multiplying the vector of the coordinates of each point of the point cloud data obtained from the first detection unit 211 by a matrix of parameters related to the position or orientation of the first detection unit 211 to the coordinates of each point, and performs coordinate transformation on each point of the point cloud data.

[0099] In step S23, the setting unit 133 divides the point cloud data into a plurality of planar point cloud data and a plurality of other point cloud data, for example, by using a plane detection algorithm and an object detection algorithm to divide the point cloud data into a plurality of planar point cloud data and a plurality of object point cloud data.

[0100] In step S24, the setting unit 133 performs optimization to minimize the sum of the errors between the planar point cloud data and the errors between the other point cloud data, similar to step S5. In step S25, the setting unit 133 performs motion distortion correction, similar to step S4.

[0101] Another example of the setting process according to the embodiment will be described with reference to Fig. 9. Fig. 9 is a diagram (3) showing an example of the setting process according to the embodiment. In Fig. 9, the setting process is performed for the first detection unit 211 and the second detection unit 212 in the order of noise removal, motion distortion correction, division, and optimization.

[0102] In step S31, the setting unit 133 performs noise removal, similar to step S1. In step S32, the setting unit 133 performs movement distortion correction, similar to step S4. In step S33, the setting unit 133 performs coordinate transformation on each point of the multiple point cloud data obtained from the first detection unit 211 and the second detection unit 212, similar to step S22.

[0103] In step S34, the setting unit 133 divides the plurality of point cloud data into a plurality of planar point cloud data and a plurality of object point cloud data, similar to step S23. In step S35, the setting unit 133 performs optimization by minimizing the sum of the errors between the planar point cloud data and the errors between the plurality of object point cloud data, similar to step S5.

[0104] Another example of the setting process according to the embodiment will be described with reference to Fig. 10. Fig. 10 is a diagram (4) showing an example of the setting process according to the embodiment. In Fig. 10, the setting process is performed for the first detection unit 211 and the second detection unit 212 in the order of noise removal, division, motion distortion correction, and optimization.

[0105] In step S41, the setting unit 133 performs noise removal in the same manner as in step S1. In step S42, the setting unit 133 performs coordinate transformation on each point of the plurality of point cloud data in the same manner as in step S22.

[0106] In step S43, the setting unit 133 divides the plurality of point cloud data into a plurality of plane point cloud data and a plurality of object point cloud data, similar to step S23. In step S44, the setting unit 133 performs motion distortion correction, similar to step S4.

[0107] In step S45, similarly to step S5, the setting unit 133 performs optimization by minimizing the sum of the errors between the plane point cloud data and the errors between the plurality of object point cloud data.

[0108] (1-4. Information Processing Procedure According to the Embodiment) Next, an example of the above-mentioned information processing procedure will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the information processing procedure according to the embodiment.

[0109] In step S101, the information processing device 100 moves the moving object 200. In step S102, the information processing device 100 acquires a plurality of pieces of detection information while moving the moving object 200. In step S103, the information processing device 100 provisionally sets parameters of the detection unit 210 based on the acquired detection information.

[0110] In step S104, the information processing device 100 determines whether the setting process has ended. If the information processing device 100 determines that the setting process has not ended (step S104; No), it provides information relating to the parameter setting process in step S105. If the information processing device 100 determines that the setting process has ended (step S104; Yes), it provides information relating to the parameter setting results in step S106. In step S107, the information processing device 100 officially sets the parameters.

[0111] (1-5. Modifications of the Embodiment) The information processing device 100 is not limited to being integrated with the mobile object 200, but may be realized as a device external to the mobile object 200, such as a device integrated with the terminal 300.

[0112] The moving body 200 is not limited to a legged robot having legs, but can be realized by a legged robot, a wheeled robot having wheels, a leg-wheeled robot having legs and wheels, a vehicle having wheels, etc., which has at least one of legs and wheels as the moving part 220.

[0113] The terminal 300 is not limited to a device that displays the provided information on a display unit, but may be realized by a device having a presentation unit that presents the provided information, such as an audio output unit that outputs the provided information as audio.

[0114] (2. Other Embodiments) The processes according to the embodiments can be implemented in various different forms other than the above-described embodiments.

[0115] Of the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. The various information shown in each drawing is not limited to the information shown in the drawings.

[0116] The components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads and usage conditions.

[0117] The above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0118] The effects described in this specification are merely examples and are not limiting, and other effects may also be present.

[0119] (3. Effects of the information processing device according to the present disclosure) As described above, the information processing device according to the present disclosure (information processing device 100 in the embodiment) includes, as control units, a movement control unit (movement control unit 131 in the embodiment) that executes a movement control procedure, an acquisition unit (acquisition unit 132 in the embodiment) that executes an acquisition procedure, and a setting unit (setting unit 133 in the embodiment) that executes a setting procedure, and executes the information processing method according to the present disclosure.

[0120] The movement control procedure involves moving a moving body (moving body 200 in this embodiment). The acquisition procedure involves acquiring multiple pieces of detection information in which the same detection target is detected by multiple detection units (detection units 210 in this embodiment) provided on the moving body while the moving body is moving. The setting procedure involves setting parameters for the detection units based on the acquired detection information.

[0121] In this way, the information processing device according to the present disclosure moves multiple detection units in accordance with the movement of the moving body, causing these detection units to detect the same detection target, and sets the parameters of the detection units based on the multiple pieces of detection information.

[0122] This allows the information processing device to remove noise by matching feature points of the same detection target in the detection information, even if there are only a few feature points of the detection target. The information processing device also performs coordinate transformation using motion distortion correction on each point of the point cloud data, and sets parameters to minimize errors between point cloud data. Therefore, the information processing device can perform calibration without using special components, even if multiple detection units are installed on a moving object.

[0123] The information processing device is integrated with a moving body, and the moving body has at least one of legs and wheels.

[0124] In this way, the information processing device can be applied to moving bodies such as legged robots, wheeled robots, leg-wheeled robots, or vehicles.

[0125] The setting unit performs at least one of noise removal for removing noise from the detection information, parameter optimization based on the detection information, and motion distortion correction for correcting motion distortion caused by the movement of the moving object.

[0126] This allows the information processing device to perform effective calibration.

[0127] The acquisition unit further acquires movement information relating to the movement of the moving object, and the setting unit performs movement distortion correction based on the movement information acquired by the acquisition unit.

[0128] This allows the information processing device to perform calibration that is effective for a moving body such as a legged robot, which is prone to motion distortion.

[0129] When the movement control unit causes the moving body to turn on the spot, the setting unit performs movement distortion correction.

[0130] By making the moving object turn on the spot, the information processing device can reduce the amount of movement of the moving object and reduce the amount of movement distortion caused by the movement of the moving object. However, moving objects such as legged robots tend to sway three-dimensionally just by turning on the spot, which can easily cause movement distortion. Therefore, the information processing device can perform particularly effective calibration for moving objects such as legged robots by correcting the movement distortion even when the moving object is turned on the spot.

[0131] The acquisition unit acquires at least one of the position, movement trajectory, and movement amount of the moving body as movement information, and the setting unit performs movement distortion correction based on at least one of the position, movement trajectory, and movement amount of the moving body acquired by the acquisition unit.

[0132] The information processing device can perform effective motion distortion correction by coordinate transformation, particularly when performing motion distortion correction based on the position, movement trajectory, or movement amount of a moving object.

[0133] The acquisition unit acquires detection information including multiple point cloud data in which detection targets are detected by each detection unit, and movement information regarding the movement of the moving body, and the setting unit performs error minimization to minimize the error between the point cloud data based on the detection information and movement information acquired by the acquisition unit as optimization.

[0134] When minimizing errors between point cloud data, the information processing device uses movement information in addition to detection information, thereby minimizing errors between point cloud data that have been coordinate-transformed based on the movement information, allowing the information processing device to set more appropriate parameters.

[0135] The setting unit divides the point cloud data into a plurality of plane point cloud data in which the same plane is detected by each of the detection units, and a plurality of object point cloud data in which the same object is detected by each of the detection units, and minimizes the sum of the error between the plane point cloud data and the error between the object point cloud data.

[0136] In this way, when minimizing the error between point cloud data, the information processing device divides the data into planar point cloud data, in which errors tend to converge, and object point cloud data, in which errors tend not to converge, thereby easily minimizing the total value of the error between planar point cloud data and the error between object point cloud data.

[0137] The setting unit further performs, as optimization, setting of at least one of the movement amount and movement trajectory of the moving body that minimizes the error between the point cloud data.

[0138] This allows the information processing device to encourage movement based on at least one of the movement amount and movement trajectory that minimizes the error between the point cloud data, making it possible to easily minimize the error.

[0139] The acquisition unit acquires feature points of the detection target as the detection information, and the setting unit removes flying pixels based on the feature points acquired by the acquisition unit to remove noise.

[0140] This allows the information processing device to remove flying pixels by matching feature points of the same detection target in the detection information, even if the number of feature points of the detection target is small.

[0141] The detection units are provided at positions or in orientations such that the detection ranges of the detection units do not overlap when the moving body is stationary.

[0142] Even with such detecting units, the information processing device can move the detecting units so that their detection ranges overlap, and cause each detecting unit to detect the same detection target.

[0143] The detection units are provided facing downward so that the ground or floor surface is included in the detection range of each detection unit.

[0144] Such a detection unit can usually only detect the ground and objects on it, which have few feature points. However, even if the detection target has few feature points, the information processing device can perform noise removal, motion distortion correction, and optimization based on the same detection target in the detection information.

[0145] The detector is fixed to the moving body.

[0146] Even if the detection units are unable to overlap their detection ranges when the moving body is stationary, the information processing device moves the detection units as the moving body moves, and therefore can overlap their detection ranges.

[0147] The movement control unit moves the moving body so that the detection target is included in the detection range of each of the detection units.

[0148] Even if the detection units are unable to overlap their detection ranges when the moving body is stationary, the information processing device can detect the same detection target by overlapping their detection ranges.

[0149] The setting unit sets a parameter relating to the position or orientation of each of the detection units as the parameter.

[0150] This allows the information processing device to determine the position or orientation of each detection unit. Furthermore, the information processing device uses this parameter to unify the vectors of the coordinates of the points of each point cloud data from a coordinate system based on the position or orientation of each detection unit to a world coordinate system, thereby easily minimizing errors between point cloud data.

[0151] The information processing device further includes a providing unit that provides information relating to at least one of the process and result of parameter setting by the setting unit.

[0152] This allows the information processing device to inform the user whether there are any problems with the parameter setting process or results.

[0153] The providing unit provides information to be provided that includes a convergence rate of the error obtained by dividing a preset allowable error value by the error between the pieces of detection information acquired by the acquiring unit.

[0154] This allows the information processing device to inform the user how much further the moving object needs to be moved before the error is likely to converge.

[0155] The providing unit provides information to be provided, the information including promotion information that promotes at least one of movement of the moving body and detection of a new detection target.

[0156] This allows the information processing device to advise the user on how to move the moving body and what kind of detection target to detect in order to further reduce errors.

[0157] (4. Hardware Configuration) Information devices such as the information processing device 100 according to each of the above-described embodiments are realized by a computer 1000 configured as shown in FIG. 12. FIG. 12 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the present disclosure. The computer 1000 includes a CPU 1100, a RAM 1200, a ROM (Read Only Memory) 1300, a HDD (Hard Disk Drive) 1400, a communication interface 1500, and an input / output interface 1600. The components of the computer 1000 are connected by a bus 1050.

[0158] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. The CPU 1100 loads the programs stored in the ROM 1300 or the HDD 1400 into the RAM 1200 and executes processing corresponding to the various programs.

[0159] The ROM 1300 stores boot programs such as a Basic Input Output System (BIOS) that is executed by the CPU 1100 when the computer 1000 starts up, and programs that depend on the hardware of the computer 1000 .

[0160] HDD 1400 is a computer-readable recording medium that non-temporarily records programs executed by CPU 1100 and data used by such programs. Specifically, HDD 1400 is a recording medium that records an information processing program according to the present disclosure, which is an example of program data 1450.

[0161] The communication interface 1500 is an interface for connecting the computer 1000 to an external network 1550 (such as the Internet). The CPU 1100 receives data from other devices and transmits data generated by the CPU 1100 to other devices via the communication interface 1500.

[0162] The input / output interface 1600 is an interface for connecting the input / output device 1650 and the computer 1000. The CPU 1100 receives data from input devices such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 transmits data to output devices such as a display, a speaker, and a printer via the input / output interface 1600. The input / output interface 1600 may function as a media interface for reading a program recorded on a predetermined recording medium.

[0163] The media may be optical recording media such as DVDs (Digital Versatile Discs) and PDs (Phase Change Rewritable Discs), magneto-optical recording media such as MOs (Magneto-Optical disks), tape media, magnetic recording media, or semiconductor memories.

[0164] When the computer 1000 functions as the information processing device 100 according to the embodiment, the CPU 1100 of the computer 1000 executes an information processing program loaded onto the RAM 1200 to realize the functions of the control unit 130. The information processing program according to the present disclosure and data in the storage unit 120 are stored in the HDD 1400.

[0165] The CPU 1100 reads and executes the program data 1450 from the HDD 1400 , but as another example, the CPU 1100 may obtain these programs from other devices via an external network 1550 .

[0166] The present technology may also be configured as follows. (1) An information processing device comprising: a movement control unit that moves a moving body; an acquisition unit that acquires a plurality of pieces of detection information in which the same detection target is detected by each of a plurality of detection units provided on the moving body while the moving body is moved by the movement control unit; and a setting unit that sets parameters of the detection units based on the detection information acquired by the acquisition unit. (2) The information processing device according to (1), wherein the information processing device is integrated with the moving body, and the moving body has at least one of legs and wheels. (3) The information processing device according to (2), wherein the setting unit performs at least one of noise removal that removes noise from the detection information, parameter optimization based on the detection information, and movement distortion correction that corrects movement distortion caused by movement of the moving body. (4) The information processing device according to (3), wherein the acquisition unit further acquires movement information related to the movement of the moving body, and the setting unit performs the movement distortion correction based on the movement information acquired by the acquisition unit. (5) The information processing device according to (4), wherein the setting unit performs the movement distortion correction when the movement control unit causes the moving body to turn its position. (6) The information processing device according to (4) or (5), wherein the acquisition unit acquires at least one of a position, a movement trajectory, and a movement amount of the moving body as the movement information, and the setting unit performs the movement distortion correction based on at least one of the position, the movement trajectory, and the movement amount of the moving body acquired by the acquisition unit. (7) The information processing device according to any one of (3) to (6), wherein the acquisition unit acquires the detection information including a plurality of point cloud data in which the detection targets are detected by the detection units, and movement information regarding the movement of the moving body, and the setting unit performs error minimization, as the optimization, to minimize an error between the point cloud data based on the detection information and the movement information acquired by the acquisition unit.(8) The information processing device according to (7), wherein the setting unit divides the point cloud data into a plurality of plane point cloud data in which the same plane is detected by each of the detection units and a plurality of object point cloud data in which the same object is detected by each of the detection units, and minimizes a sum of an error between the plane point cloud data and an error between the object point cloud data. (9) The information processing device according to (7) or (8), wherein the setting unit further sets at least one of a movement amount and a movement trajectory of the moving object that minimizes the error between the point cloud data, as the optimization. (10) The information processing device according to any one of (3) to (9), wherein the acquisition unit acquires feature points of the detection target as the detection information, and the setting unit removes flying pixels based on the feature points acquired by the acquisition unit, as the noise removal. (11) The information processing device according to (2), wherein the detection unit is provided at a position or orientation such that the detection ranges of the detection units do not overlap when the moving object is stationary. (12) The information processing device according to (11), wherein the detection units are provided facing downward so that the ground or floor surface is included in the detection range of each of the detection units. (13) The information processing device according to (11) or (12), wherein the detection units are fixed to the moving body. (14) The information processing device according to any one of (11) to (13), wherein the movement control unit moves the moving body so that the detection target is included in the detection range of each of the detection units. (15) The information processing device according to any one of (11) to (14), wherein the setting unit sets a parameter related to the position or orientation of each of the detection units as the parameter. (16) The information processing device according to any one of (1) to (15), further comprising a providing unit that provides provision information related to at least one of a process and a result of setting the parameter by the setting unit. (17) The information processing device according to (16), wherein the providing unit provides the provided information including a convergence rate of an error obtained by dividing a preset allowable error value by an error between the pieces of detection information acquired by the acquiring unit.(18) The information processing device according to (16) or (17), wherein the providing unit provides the provided information including prompting information that prompts at least one of movement of the moving body and detection of a new detection target. (19) An information processing method including: a computer moving a moving body; acquiring a plurality of pieces of detection information in which the same detection target has been detected by each of a plurality of detection units provided on the moving body while the moving body is being moved; and setting parameters of the detection units based on the acquired detection information. (20) An information processing program for causing a computer to function as an information processing device comprising: a movement control unit that moves the moving body; an acquisition unit that acquires a plurality of pieces of detection information in which the same detection target has been detected by each of a plurality of detection units provided on the moving body while the moving body is being moved by the movement control unit; and a setting unit that sets parameters of the detection units based on the detection information acquired by the acquisition unit.

[0167] REFERENCE SIGNS LIST 100 Information processing device 110 Communication unit 120 Storage unit 130 Control unit 131 Movement control unit 132 Acquisition unit 133 Setting unit 134 Provision unit 200 Mobile object 210 Detection unit 211 First detection unit 212 Second detection unit 220 Movement unit

Claims

1. An information processing device comprising: a movement control unit that moves a moving body; an acquisition unit that acquires multiple pieces of detection information in which the same detection target is detected by multiple detection units provided on the moving body while the movement control unit is moving the moving body; and a setting unit that sets parameters of the detection units based on the detection information acquired by the acquisition unit.

2. The information processing device according to claim 1, wherein the information processing device is integrated with the mobile body, and the mobile body has at least one of legs and wheels.

3. The information processing device according to claim 2, wherein the setting unit performs at least one of noise removal to remove noise from the detection information, optimization of the parameters based on the detection information, and motion distortion correction to correct motion distortion caused by movement of the moving body.

4. The information processing device according to claim 3, wherein the acquisition unit further acquires movement information relating to movement of the moving object, and the setting unit performs the movement distortion correction based on the movement information acquired by the acquisition unit.

5. The information processing device according to claim 4, wherein the setting unit performs the motion distortion correction when the motion control unit causes the moving object to turn around on the spot.

6. The information processing device according to claim 4, wherein the acquisition unit acquires at least one of the position, movement trajectory, and movement amount of the moving body as the movement information, and the setting unit performs the movement distortion correction based on at least one of the position, movement trajectory, and movement amount of the moving body acquired by the acquisition unit.

7. The information processing device according to claim 3, wherein the acquisition unit acquires the detection information including a plurality of point cloud data in which the detection target is detected by each of the detection units, and movement information regarding the movement of the moving body, and the setting unit performs error minimization, as the optimization, to minimize the error between the point cloud data based on the detection information and the movement information acquired by the acquisition unit.

8. The information processing device according to claim 7, wherein the setting unit divides the point cloud data into a plurality of plane point cloud data in which the same plane is detected by each of the detection units, and a plurality of object point cloud data in which the same object is detected by each of the detection units, and minimizes the sum of the errors between the plane point cloud data and the errors between the object point cloud data.

9. The information processing device according to claim 7, wherein the setting unit further performs, as the optimization, setting at least one of the movement amount and movement trajectory of the moving object that minimizes the error between the point cloud data.

10. The information processing device according to claim 3, wherein the acquisition unit acquires feature points of the detection target as the detection information, and the setting unit removes flying pixels based on the feature points acquired by the acquisition unit to remove the noise.

11. The information processing device according to claim 2, wherein the detection units are provided at positions or in orientations such that the detection ranges of the respective detection units do not overlap when the moving body is stationary.

12. The information processing device according to claim 11, wherein the detection units are provided facing downward so that the ground or floor surface is included in the detection range of each of the detection units.

13. The information processing device according to claim 11, wherein the detection unit is fixed to the moving body.

14. The information processing device according to claim 11, wherein the movement control unit moves the moving body so that the detection target is included in the detection range of each of the detection units.

15. The information processing device according to claim 11, wherein the setting unit sets a parameter relating to the position or orientation of each of the detection units as the parameter.

16. The information processing device according to claim 1, further comprising a providing unit that provides information relating to at least one of the process and result of setting the parameters by the setting unit.

17. The information processing device according to claim 16, wherein the providing unit provides the information to be provided that includes a convergence rate of an error obtained by dividing a preset error tolerance value by the error between the pieces of detection information acquired by the acquiring unit.

18. The information processing device according to claim 16, wherein the providing unit provides the provided information including promotion information that prompts at least one of the movement of the moving body and the detection of a new detection target.

19. An information processing method including the steps of: a computer moving a mobile body; acquiring multiple pieces of detection information in which the same detection target is detected by multiple detection units provided on the mobile body while the mobile body is being moved; and setting parameters of the detection units based on the acquired detection information.

20. An information processing program for causing a computer to function as an information processing device comprising: a movement control unit that moves a moving body; an acquisition unit that acquires multiple pieces of detection information in which the same detection target is detected by multiple detection units provided on the moving body while the movement control unit is moving the moving body; and a setting unit that sets parameters of the detection units based on the detection information acquired by the acquisition unit.

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