Calibration system, calibration device, calibration method, and program

Abstract

This technology provides a method for calibrating the position and orientation of a target sensing device. [Solution] The calibration system includes a point cloud acquisition means for acquiring an object sensing point cloud from an object sensing device that measures the distance of an object whose survey reliability is guaranteed, and a calibration means for calibrating the position and orientation of the object sensing device based on the object sensing point cloud.

Description

Technical Field

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[0001] The present disclosure relates to a calibration system, a calibration device, a calibration method, and a program.

Background Art

[0002] Patent Document 1 discloses a technique for calibrating the positions and directions of a plurality of distance sensors when the plurality of distance sensors are installed in a certain area. Specifically, for each pair of distance sensors, the relative positional relationship between each pair of distance sensors is calculated by specifying the flow of pedestrians commonly observed, and based on the relative positional relationship, the positions and orientations of the plurality of distance sensors are calibrated.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the configuration of Patent Document 1 described above, there is a problem that the presence of pedestrians that can be commonly observed for each pair of distance sensors is required as a premise for calibrating the positions and orientations of the plurality of distance sensors.

[0005] By the way, for example, when performing fixed-point observation of a road surface using a LiDAR device installed on the side or above a road to detect foreign objects on the road surface, it may be considered to perform coordinate conversion of the position of the foreign object expressed in the LiDAR coordinate system into a geographical coordinate system and output it. Since the installation position and installation posture of the LiDAR device expressed in the geographical coordinate system are known, it can be said that the coordinate conversion matrix for coordinate conversion from the LiDAR coordinate system to the geographical coordinate system is also known.

[0006] However, the installation location and orientation of LiDAR devices can change constantly due to environmental factors such as ambient temperature, vibration, wind, and terrain changes. Therefore, it is necessary to grasp the amount of deviation from the design values ​​of the installation location and orientation of LiDAR devices in real time.

[0007] In particular, when the distance from the LiDAR device to the foreign object is 100 meters and it is desired to detect the position of the foreign object with centimeter-order accuracy, the deviation from the design value that is permissible for the installation orientation of the LiDAR device will be less than 0.01 degrees. Therefore, it is necessary to determine the above deviation amount with high precision.

[0008] The purpose of this disclosure is to provide a technology for calibrating the position and orientation of a target sensing device. [Means for solving the problem]

[0009] A calibration system is provided, which includes a point cloud acquisition means for acquiring a point cloud from an object sensing device that measures the distance of an object whose survey reliability is guaranteed, and a calibration means for calibrating the position and orientation of the object sensing device based on the object sensing point cloud.

[0010] A calibration device is provided, which includes a point cloud acquisition means for acquiring an object sensing point cloud from an object sensing device that measures the distance of an object whose survey reliability is guaranteed, and a calibration means for calibrating the position and orientation of the object sensing device based on the object sensing point cloud.

[0011] A calibration method is provided in which a computer acquires an object sensing point cloud from an object sensing device that measures the distance of an object whose survey reliability is guaranteed, and calibrates the position and orientation of the object sensing device based on the object sensing point cloud.

[0012] A program is provided to operate a computer as point cloud acquisition means for acquiring a target sensing point cloud from a target sensing device that measures the distance to a target object with guaranteed survey reliability, and calibration means for calibrating the position and orientation of the target sensing device based on the target sensing point cloud.

Effect of the Invention

[0013] According to the present disclosure, the position and orientation of the target sensing device can be calibrated.

Brief Description of the Drawings

[0014] [Figure 1] It is a block diagram of a calibration system. [Figure 2] It is a control flow of a calibration system. [Figure 3] It is a schematic diagram of a road monitoring system. <00​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The calibration means 102 calibrates the position and orientation of the target sensing device based on the target sensing point cloud.

[0018] Next, the operation of the calibration system 100 will be described. FIG. 2 shows the control flow of the calibration system 100.

[0019] First, the point cloud acquisition means 101 acquires a target sensing point cloud from a target sensing device that measures the distance to an object for which measurement reliability is guaranteed (S101). Next, the calibration means 102 calibrates the position and orientation of the target sensing device based on the target sensing point cloud (S102). According to the above configuration, the position and orientation of the target sensing device can be calibrated.

[0020] Note that calibrating the position and orientation of the target sensing device does not mean physically modifying the position and orientation of the target sensing device. Calibrating the position and orientation of the target sensing device typically means grasping the amount of deviation from the design value of the position and orientation of the target sensing device or correcting the output data of the target sensing device using the calculated amount of deviation.

[0021] (First Embodiment) Next, the road monitoring system according to the first embodiment of the present disclosure will be described.

[0022] Hereinafter, the present invention will be described through embodiments of the invention, but the invention according to the claims is not limited to the following embodiments. Also, not all of the configurations described in the embodiments are essential as means for solving the problems. For clarity of explanation, the following description and drawings have been appropriately omitted and simplified. In each drawing, the same elements are denoted by the same reference numerals, and duplicate explanations are omitted as necessary.

[0023] In the following embodiments, the description will be divided into multiple sections or embodiments where necessary for convenience. Unless otherwise specified, these are not unrelated, and one may be a modification, application, detailed explanation, or supplementary explanation of part or all of the other. Furthermore, in the following embodiments, when referring to the number of elements (including number, numerical value, quantity, and range), unless otherwise specified or clearly limited to a specific number in principle, it is not limited to that specific number, and may be greater than or less than that number.

[0024] Furthermore, in the following embodiments, the components (including operation steps, etc.) are not necessarily essential unless specifically stated or considered to be fundamentally essential. Similarly, in the following embodiments, when referring to the shape or positional relationship of components, etc., it shall include those substantially similar to or resembling their shape, etc., unless specifically stated or considered to be fundamentally different. The same applies to the numbers, etc. (including number, numerical value, quantity, and range) mentioned above.

[0025] Figure 3 is a schematic diagram of the road monitoring system 1. The road monitoring system 1 is a specific example of a calibration system. As shown in Figure 3, the road monitoring system 1 includes a road monitoring device 2 and a plurality of fixed-point observation devices 4.

[0026] The road monitoring device 2 and the multiple fixed-point observation devices 4 typically communicate bidirectionally via a WAN (Wide Area Network) such as the Internet. The multiple fixed-point observation devices 4 are permanently installed to the side or above the road 6. Each of the multiple fixed-point observation devices 4 is typically fixed to a pole located to the side of the road 6. The road monitoring device 2 may be implemented by a single device or by distributed processing using multiple devices.

[0027] Each fixed-point observation device 4 is a specific example of a sensing device installed to sense the road 6. Each fixed-point observation device 4 is a specific example of a fixed-point observation sensing device installed to sense the road 6. In this embodiment, each fixed-point observation device 4 is a LiDAR (Light Detection And Ranging) device. Therefore, each fixed-point observation device 4 measures the distance of the space including the road surface of the road 6 to generate a 3D point cloud and outputs the generated 3D point cloud to the road monitoring device 2. The 3D point cloud is a specific example of a sensing point cloud.

[0028] However, instead, each fixed-point observation device 4 may be a radar device (Radio Detection and Ranging) or a stereo camera. In this case as well, each fixed-point observation device 4 measures the distance of the space including the road surface of the road 6 to generate a 3D point cloud and outputs the generated 3D point cloud to the road monitoring device 2.

[0029] In this embodiment, the multiple fixed-point observation devices 4 include a reference sensing device 4a and a target sensing device 4b. The reference sensing device 4a and the target sensing device 4b are arranged apart from each other along the road 6. The sensing range P of the reference sensing device 4a and the sensing range Q of the target sensing device 4b overlap. In Figure 3, the sensing overlap region R of the sensing ranges P and Q is shown by hatching. The sensing overlap region R is a specific example of an object for which survey reliability is guaranteed. The differences between the reference sensing device 4a and the target sensing device 4b are as follows.

[0030] The reference sensing device 4a is a sensing device whose survey reliability is guaranteed. The guaranteed survey reliability of the reference sensing device 4a means that the survey reliability of the reference sensing device 4a's position and orientation is guaranteed. Here, the position and orientation of the reference sensing device 4a refers to the position and orientation of the reference sensing device 4a expressed in a geographic coordinate system. The survey reliability of the position and orientation of the reference sensing device 4a is guaranteed by periodically surveying it. Since the position and orientation of the reference sensing device 4a inevitably changes over time, it naturally deviates from its design value. Therefore, the guaranteed survey reliability of the position and orientation of the reference sensing device 4a can be rephrased as the amount of deviation from the design value of the position and orientation of the reference sensing device 4a being accurately measured.

[0031] In contrast, the target sensing device 4b is a sensing device whose survey reliability is not guaranteed. That is, the survey reliability of the position and orientation of the target sensing device 4b is not guaranteed. Therefore, the amount of deviation from the design value of the position and orientation of the target sensing device 4b has not been measured.

[0032] The reference sensing device 4a outputs the 3D point cloud generated by distance measurement as the reference sensing point cloud to the road monitoring device 2. The target sensing device 4b outputs the 3D point cloud generated by distance measurement as the target sensing point cloud to the road monitoring device 2.

[0033] Therefore, the road monitoring system 1 calibrates the position and orientation of the target sensing device 4b, as will be described later. Typically, this involves calculating the deviation of the position and orientation of the target sensing device 4b from its design value, or using the calculated deviation to appropriately correct the output data of the target sensing device 4b.

[0034] Figure 4 shows a block diagram of the road monitoring device 2. The road monitoring device 2 is a specific example of a calibration device. As shown in Figure 4, the road monitoring device 2 includes a point cloud acquisition unit 10, a calibration unit 11, an anomaly detection unit 12, and an output unit 13.

[0035] The point cloud acquisition unit 10 acquires a reference sensing point cloud from the reference sensing device 4a and acquires a target sensing point cloud from the target sensing device 4b.

[0036] The calibration unit 11 calibrates the position and orientation of the target sensing device 4b based on the reference sensing point cloud and the target sensing point cloud. Specifically, the calibration unit 11 calibrates the position and orientation of the target sensing device 4b based on the comparison result obtained by comparing the reference sensing point cloud and the target sensing point cloud. The comparison result obtained by comparing the reference sensing point cloud and the target sensing point cloud refers to the registration result obtained by registering the reference sensing point cloud and the target sensing point cloud. ICP (Iterative Closest Point) is a known method for registering the reference sensing point cloud and the target sensing point cloud. ICP is a technique that derives the relative positional relationship between the LiDAR coordinate system of the reference sensing point cloud and the LiDAR coordinate system of the target sensing point cloud by associating the reference sensing point cloud and the target sensing point cloud. Specifically, ICP generates a rigid body transformation matrix to convert the LiDAR coordinate system of the reference sensing point cloud to the LiDAR coordinate system of the target sensing point cloud. The rigid body transformation matrix is ​​a concrete example of the comparison and alignment results.

[0037] The calibration unit 11 performs a two-stage alignment process, coarse adjustment and fine adjustment, from the viewpoint of speeding up the alignment process by ICP. In the coarse adjustment, the calibration unit 11 generates initial alignment conditions for the reference sensing point cloud and the target sensing point cloud based on the design values ​​of the position and orientation of the reference sensing device 4a and the target sensing device 4b. Alternatively, in the coarse adjustment, the calibration unit 11 may generate initial alignment conditions for the reference sensing point cloud and the target sensing point cloud based on the self-position estimated by the self-position estimation means mounted on the reference sensing device 4a and the target sensing device 4b. The self-position estimation means is typically a GNSS module (Global Navigation Satellite System). Examples of GNSS modules include GPS modules (Global Positioning System), GLONASS modules (Global Navigation Satellite System), Galileo modules, BeiDou modules, and QZSS modules (Quasi-Zenith Satellite System). Then, in the fine adjustment, the calibration unit 11 aligns the reference sensing point cloud and the target sensing point cloud using ICP.

[0038] The calibration unit 11 may, from the viewpoint of speeding up the alignment process by ICP, extract point clouds favorable to ICP from the reference sensing point cloud and the target sensing point cloud, and then align the extracted point clouds. In this case, the calibration unit 11 extracts point clouds for ICP that correspond to structurally distinctive objects such as utility poles, signs, and curbs installed on road 6, or to white lines that can be identified by brightness. In this case, the calibration unit 11 can typically use feature point extraction using Harris3D or FPFH (Fast Point Feature Histograms).

[0039] From the viewpoint of speeding up the alignment process by ICP, the calibration unit 11 may search for and identify the sensing overlap region R based on the design values ​​of the position and orientation of the reference sensing device 4a and the target sensing device 4b, extract the point cloud to which the sensing overlap region R belongs from the reference sensing point cloud and the target sensing point cloud, and align the extracted point clouds.

[0040] The calibration unit 11 then uses a rigid body transformation matrix to calibrate the position and orientation of the target sensing device 4b. Specifically, the position and orientation of the reference sensing device 4a is accurately measured by surveying, and the rigid body transformation matrix determines the positional relationship between the LiDAR coordinate system of the reference sensing device 4a and the LiDAR coordinate system of the target sensing device 4b. Therefore, the position and orientation of the target sensing device 4b can be determined with substantially the same accuracy as that of the reference sensing device 4a. In other words, the amount of deviation from the design value of the position and orientation of the target sensing device 4b can be determined with high accuracy. The calibration unit 11 generates a calibration transformation matrix that shows this amount of deviation.

[0041] Typical timings for the calibration unit 11 to perform the above calibration include periodic timing, timing immediately after an earthquake occurs, and timing instructed by the operator of the road monitoring device 2. Alternatively, the calibration may be performed when the degree of agreement between two different target sensing point clouds on the time axis falls below a predetermined value, after monitoring the target sensing point cloud output from the target sensing device 4b. In short, the timing of the calibration can be determined based on the rigid body transformation matrix obtained by aligning two different target sensing point clouds on the time axis.

[0042] The abnormality detection unit 12 determines whether or not there is an abnormality in the road 6 based on the target sensing point cloud. An abnormality in the road 6 is typically a foreign object present on the road surface of the road 6, or a localized bulge or depression in the road 6. The abnormality detection unit 12 can determine whether or not there is an abnormality in the road 6 using, for example, PointNet. Alternatively, the abnormality detection unit 12 may detect foreign objects present on the road surface of the road 6 as abnormalities by detecting the road surface of the road 6 and detecting a point cloud that is separated above the road surface by a predetermined distance or more. If the abnormality detection unit 12 detects an abnormality in the road 6, it converts the location of the abnormality into a geographic coordinate system and outputs it to the output unit 13. Furthermore, in order for the anomaly detection unit 12 to convert the location of the anomaly to a geographic coordinate system, it calibrates the position and orientation of the target sensing device 4b by applying a calibration transformation matrix generated by the calibration unit 11 to the position and orientation of the target sensing device 4b, and then transforms the location of the anomaly, expressed in the LiDAR coordinate system of the target sensing device 4b, to a geographic coordinate system based on the position and orientation of the target sensing device 4b after calibration.

[0043] When the abnormality detection unit 12 detects an abnormality in the road 6, the output unit 13 outputs an abnormality avoidance command to one or more vehicles traveling near the abnormality. The abnormality avoidance command typically includes the location of the abnormality expressed in a geographic coordinate system. The vehicles perform autonomous avoidance control based on the comparison result of comparing their own position with the location of the abnormality. In addition to outputting an abnormality avoidance command to one or more vehicles traveling near the abnormality, the output unit 13 may also notify the administrator of the road 6.

[0044] Next, we will explain the operation of the road monitoring device 2. Figure 5 shows the operation flow of the road monitoring device 2.

[0045] As shown in Figure 5, the point cloud acquisition unit 10 first acquires a reference sensing point cloud from the reference sensing device 4a and then acquires a target sensing point cloud from the target sensing device 4b (S200). Next, the calibration unit 11 calibrates the position and orientation of the target sensing device 4b based on the reference sensing point cloud and the target sensing point cloud (S210). Next, the anomaly determination unit 12 determines whether or not there is an anomaly in the road 6 based on the target sensing point cloud (S220). Then, if the anomaly determination unit 12 detects an anomaly in the road 6, the output unit 13 outputs an anomaly avoidance command to one or more vehicles traveling near the anomaly (S230).

[0046] The first embodiment of this disclosure has been described above, and the first embodiment has the following features.

[0047] The road monitoring system 1 (calibration system) includes a point cloud acquisition unit 10 (point cloud acquisition means) that acquires a target sensing point cloud from a target sensing device 4b that measures distance within a sensing overlap area R (an object whose survey reliability is guaranteed), and a calibration unit 11 (calibration means) that calibrates the position and orientation of the target sensing device 4b based on the target sensing point cloud. With the above configuration, the position and orientation of the target sensing device 4b can be calibrated.

[0048] Furthermore, the object whose survey reliability is guaranteed is the sensing overlap region R (object) that can be measured by the reference sensing device 4a, whose survey reliability is guaranteed. The point cloud acquisition unit 10 acquires the reference sensing point cloud generated by the reference sensing device 4a measuring the sensing overlap region R. The calibration unit 11 calibrates the position and orientation of the target sensing device 4b based on the comparison result obtained by comparing the reference sensing point cloud and the target sensing point cloud. With the above configuration, as long as the reference sensing device 4a and the target sensing device 4b can measure the sensing overlap region R that they have in common, the position and orientation of the target sensing device 4b can be calibrated even if the reference sensing device 4a and the target sensing device 4b are far apart.

[0049] The object for which survey reliability is guaranteed is the sensing overlap region R (overlapping region) where the sensing range of the reference sensing device 4a and the sensing range of the target sensing device 4b overlap. With the above configuration, the position and orientation of the target sensing device 4b can be calibrated even if the reference sensing device 4a and the target sensing device 4b are far apart.

[0050] Furthermore, the comparison result obtained by comparing the reference sensing point cloud and the target sensing point cloud is the registration result obtained by aligning the reference sensing point cloud and the target sensing point cloud. With the above configuration, the position and orientation of the target sensing device 4b can be calibrated using an existing ICP.

[0051] (Second Embodiment) Next, a second embodiment of this disclosure will be described. The following description will focus on the differences between this embodiment and the first embodiment, omitting any redundant explanations. Figure 6 is a schematic diagram of the road monitoring system 1.

[0052] As shown in Figure 6, in this embodiment, the reference sensing device 4a is within the sensing range Q of the target sensing device 4b. That is, the target sensing device 4b can generate a point cloud corresponding to the reference sensing device 4a by measuring the distance to the reference sensing device 4a. In this embodiment, the object for which survey reliability is guaranteed is the reference sensing device 4a itself, for which survey reliability is guaranteed.

[0053] The calibration unit 11 then calibrates the position and orientation of the target sensing device 4b based on the target sensing point cloud. Specifically, the calibration unit 11 aligns the point cloud of the reference sensing device 4a, assuming that the position and orientation of the target sensing device 4b matches the design value, with the target sensing point cloud acquired by the point cloud acquisition unit 10 during calibration. This allows the amount of deviation from the design value of the position and orientation of the target sensing device 4b to be determined with high accuracy. That is, the calibration unit 11 can generate a calibration transformation matrix that shows the amount of deviation. The point cloud of the reference sensing device 4a, assuming that the position and orientation of the target sensing device 4b matches the design value, can typically be created using CAD data of the reference sensing device 4a.

[0054] The second embodiment has been described above, and the second embodiment has the following features.

[0055] In other words, the object whose survey reliability is guaranteed is the reference sensing device 4a itself, whose survey reliability is guaranteed. With the above configuration, even when it is difficult to align the reference sensing point cloud with the reference sensing point cloud using ICP, such as when the road surface of the road 6 is raised over a wide area, the position and orientation of the target sensing device 4b can be calibrated.

[0056] (Third embodiment) Next, a third embodiment of this disclosure will be described. The following description will focus on the differences between this embodiment and the first embodiment, omitting any redundant explanations. Figure 7 is a schematic diagram of the road monitoring system 1.

[0057] As shown in Figure 7, in this embodiment, the multiple fixed-point observation devices 4 include sensing devices 4a, 4b, 4c, 4d, 4e, and 4f. Sensing devices 4a and 4f correspond to reference sensing devices, which are sensing devices with guaranteed survey reliability. Sensing devices 4b through 4e correspond to target sensing devices, which are sensing devices for which survey reliability is not guaranteed.

[0058] In the first and second embodiments described above, the position and orientation of sensing device 4b was calibrated using sensing device 4a as a reference. In contrast, the road monitoring device 2 of this embodiment further calibrates the position and orientation of sensing device 4c using sensing device 4b as a reference, and then calibrates the position and orientation of sensing device 4d using sensing device 4c as a reference. However, if such calibration is repeated, unavoidable errors during alignment will accumulate, making it impossible to achieve high calibration accuracy for the position and orientation of the downstream fixed-point observation device 4. Therefore, in the example of Figure 7, the calibration of the position and orientation of sensing devices 4d and 4e may be performed using sensing device 4f as a reference. That is, the position and orientation of sensing device 4e can be calibrated using sensing device 4f as a reference, and then the position and orientation of sensing device 4d can be calibrated using sensing device 4e as a reference. In this way, when calibrating the position and orientation of multiple fixed-point observation devices 4, it is conceivable to select a reference sensing device for each fixed-point observation device 4 so as to minimize the cumulative number of calibrations from the reference sensing device.

[0059] (Fourth Embodiment) Next, a fourth embodiment of this disclosure will be described. The following description will focus on the differences between this embodiment and the second embodiment described above, omitting any redundant explanations. Figure 8 is a schematic diagram of the road monitoring system 1.

[0060] As shown in Figure 8, in this embodiment, the control point marker 14, whose survey reliability is guaranteed, is within the sensing range Q of the target sensing device 4b. That is, the target sensing device 4b can generate a point cloud corresponding to the control point marker 14 by measuring the distance to the control point marker 14. In this embodiment, the object whose survey reliability is guaranteed is the control point marker 14, whose survey reliability is guaranteed. The control point marker 14 is typically a triangulation point or a leveling point.

[0061] The calibration unit 11 then calibrates the position and orientation of the target sensing device 4b based on the target sensing point cloud. Specifically, the calibration unit 11 aligns the point cloud of the reference point marker 14, assuming that the position and orientation of the target sensing device 4b matches the design value, with the target sensing point cloud acquired by the point cloud acquisition unit 10 during calibration. This allows the amount of deviation from the design value of the position and orientation of the target sensing device 4b to be determined with high accuracy. That is, the calibration unit 11 can generate a calibration transformation matrix that shows the amount of deviation. The point cloud of the reference point marker 14, assuming that the position and orientation of the target sensing device 4b matches the design value, can typically be created using CAD data of the reference point marker 14.

[0062] The fourth embodiment has been described above, and the fourth embodiment has the following features.

[0063] In other words, the object whose survey reliability is guaranteed is the reference point marker 14 whose survey reliability is guaranteed. With the above configuration, even when it is difficult to align the reference sensing point cloud with the reference sensing point cloud using ICP due to the presence of many foreign objects scattered on the road 6, the position and orientation of the target sensing device 4b can be calibrated.

[0064] The fourth embodiment described above can be modified as follows: Instead of the control point marker 14 with guaranteed survey reliability, a structure with guaranteed survey reliability may be used. The survey reliability of the structure is guaranteed, for example, by surveying it using the control point marker 14 with guaranteed survey reliability. Also, when the structure is a rectangular parallelepiped or a cube, the attitude calibration of the target sensing device 4b may be performed using the edges of the structure.

[0065] <Example hardware configuration> The following describes how each functional configuration of the road monitoring device 2 is realized through a combination of hardware and software.

[0066] Figure 9 is a block diagram illustrating the hardware configuration of a computer. The device in this disclosure can realize the above-described functions using a computer 500 including the hardware configuration shown in Figure 9. The computer 500 may be a portable computer such as a smartphone or tablet terminal, or a stationary computer such as a PC. The computer 500 may be a dedicated computer designed to realize each device, or it may be a general-purpose computer. The computer 500 can realize the corresponding functions by installing a predetermined program.

[0067] Computer 500 has a bus 502, a processor 504, memory 506, a storage device 508, an input / output interface 510 (an interface is also called an I / F (Interface)), and a network interface 512. Bus 502 is a data transmission path for the processor 504, memory 506, storage device 508, input / output interface 510, and network interface 512 to send and receive data to and from each other. However, the method of connecting the processor 504 and the other components to each other is not limited to bus connection.

[0068] Processor 504 is a variety of processors such as a CPU, GPU, or FPGA. Memory 506 is main memory implemented using RAM (Random Access Memory), etc.

[0069] The storage device 508 is an auxiliary storage device implemented using a hard disk, SSD, memory card, or ROM (Read Only Memory). The storage device 508 stores a program for implementing a predetermined function. The processor 504 reads this program into memory 506 and executes it to implement each functional component of each device.

[0070] The input / output interface 510 is an interface for connecting the computer 500 with input / output devices. For example, input devices such as keyboards and output devices such as display devices are connected to the input / output interface 510.

[0071] Network interface 512 is an interface for connecting computer 500 to a network.

[0072] The above describes examples of hardware configurations in this disclosure, but the embodiments described above are not limited thereto. This disclosure can also be implemented by having a processor execute a computer program to perform any processing.

[0073] In the examples described above, the program includes a set of instructions (or software code) that, when loaded into a computer, cause the computer to perform one or more of the functions described in the embodiments. The program may be stored on a non-temporary computer-readable medium or a physical storage medium. Examples, but not limited to, include random-access memory (RAM), read-only memory (ROM), flash memory, solid-state drive (SSD) or other memory technologies, CD-ROM, digital versatile disc (DVD), Blu-ray® disc or other optical disc storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices. The program may be transmitted over a temporary computer-readable medium or a communication medium. Examples, but not limited to, include temporary computer-readable medium or a communication medium that includes electrical, optical, acoustic or other forms of propagating signals.

[0074] Although the present disclosure has been described above with reference to embodiments, the present disclosure is not limited to the embodiments described above. Various modifications to the structure and details of the present disclosure can be understood by those skilled in the art within the scope of the present disclosure.

[0075] Each drawing is merely illustrative to illustrate one or more embodiments. Each drawing may be associated with one or more other embodiments rather than with only one specific embodiment. As those skilled in the art will understand, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings, for example, to create embodiments not explicitly shown or described. Not all features or steps shown in any one drawing to illustrate an exemplary embodiment are necessarily required, and some features or steps may be omitted. The order of steps shown in any of the drawings may be changed as appropriate.

[0076] Some or all of the above embodiments may also be described as follows, but are not limited to the following: (Note 1) A point cloud acquisition means for acquiring a point cloud of an object sensing device that measures the distance of an object whose survey reliability is guaranteed, Calibration means for calibrating the position and orientation of the target sensing device based on the target sensing point cloud, including, Calibration system. (Note 2) The calibration system described in Appendix 1, The aforementioned object is an object whose distance can be measured by a reference sensing device whose survey reliability is guaranteed. The point cloud acquisition means acquires the reference sensing point cloud generated by the reference sensing device measuring the distance to the object, The calibration means calibrates the position and orientation of the target sensing device based on the comparison result obtained by comparing the reference sensing point cloud with the target sensing point cloud. Calibration system. (Note 3) The calibration system described in Appendix 2, The object is an overlapping region where the sensing range of the reference sensing device and the sensing range of the target sensing device overlap. Calibration system. (Note 4) The calibration system described in Appendix 2, The comparison result obtained by comparing the reference sensing point cloud and the target sensing point cloud is the registration result obtained by registering the reference sensing point cloud and the target sensing point cloud. Calibration system. (Note 5) The calibration system described in Appendix 1, The aforementioned object is the reference sensing device itself, whose surveying reliability is guaranteed. Calibration system. (Note 6) The calibration system described in Appendix 1, The aforementioned object is a structure whose survey reliability is guaranteed, or a control point marker whose survey reliability is guaranteed. Calibration system. (Note 7) The calibration system described in Appendix 1, Furthermore, An anomaly determination means for determining anomalies based on the aforementioned target sensing point cloud, An output means for outputting the determination result by the abnormality determination means, including, Calibration system. (Note 8) A point cloud acquisition means for acquiring a point cloud of an object sensing device that measures the distance of an object whose survey reliability is guaranteed, Calibration means for calibrating the position and orientation of the target sensing device based on the target sensing point cloud, including, Calibration device. (Note 9) Computers A target sensing point cloud is acquired from a target sensing device that measures the distance of an object whose survey reliability is guaranteed. Based on the aforementioned target sensing point cloud, the position and orientation of the target sensing device are calibrated. Calibration method. (Note 10) Computers A point cloud acquisition means for acquiring a point cloud of an object sensing device that measures the distance of an object whose survey reliability is guaranteed, Calibration means for calibrating the position and orientation of the target sensing device based on the target sensing point cloud, To make it work as program.

[0077] Some or all of the elements (e.g., configuration and function) described in Appendices 2 to 7 that are dependent on Appendice 1 may also be dependent on Appendices 8 to 10 in the same way as in Appendices 2 to 7. Some or all of the elements described in any appendice may be applicable to various hardware, software, recording means, systems, and methods for recording software. [Explanation of Symbols]

[0078] 1. Road monitoring system 2 Road monitoring device 4. Fixed-point observation device 4a Reference sensing device 4b Target sensing device 4a Sensing device 4b Sensing device 4c sensing device 4D sensing device 4e Sensing device 4f Sensing Device 6 road 10 Point cloud acquisition section 11. Proofreading Department 12 Abnormality determination section 13 Output section 14 Reference point sign P Sensing Range Q Sensing range R sensing overlapping region

Claims

1. A point cloud acquisition means for acquiring a point cloud of an object sensing device that measures the distance of an object whose survey reliability is guaranteed, Calibration means for calibrating the position and orientation of the target sensing device based on the target sensing point cloud, including, Calibration system.

2. A calibration system according to claim 1, The aforementioned object is an object whose distance can be measured by a reference sensing device whose survey reliability is guaranteed. The point cloud acquisition means acquires the reference sensing point cloud generated by the reference sensing device measuring the distance to the object, The calibration means calibrates the position and orientation of the target sensing device based on the comparison result obtained by comparing the reference sensing point cloud with the target sensing point cloud. Calibration system.

3. A calibration system according to claim 2, The object is an overlapping region where the sensing range of the reference sensing device and the sensing range of the target sensing device overlap. Calibration system.

4. A calibration system according to claim 2, The comparison result obtained by comparing the reference sensing point cloud and the target sensing point cloud is the registration result obtained by registering the reference sensing point cloud and the target sensing point cloud. Calibration system.

5. A calibration system according to claim 1, The aforementioned object is the reference sensing device itself, whose surveying reliability is guaranteed. Calibration system.

6. A calibration system according to claim 1, The aforementioned object is a structure whose survey reliability is guaranteed, or a control point marker whose survey reliability is guaranteed. Calibration system.

7. A calibration system according to claim 1, Furthermore, An anomaly determination means for determining anomalies based on the aforementioned target sensing point cloud, An output means for outputting the determination result by the abnormality determination means, including, Calibration system.

8. A point cloud acquisition means for acquiring a point cloud of an object sensing device that measures the distance of an object whose survey reliability is guaranteed, Calibration means for calibrating the position and orientation of the target sensing device based on the target sensing point cloud, including, Calibration device.

9. Computers A target sensing point cloud is acquired from a target sensing device that measures the distance of an object whose survey reliability is guaranteed. Based on the aforementioned target sensing point cloud, the position and orientation of the target sensing device are calibrated. Calibration method.

10. Computers A point cloud acquisition means for acquiring a point cloud of an object sensing device that measures the distance of an object whose survey reliability is guaranteed, Calibration means for calibrating the position and orientation of the target sensing device based on the target sensing point cloud, To make it work as program.

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