Three-dimensional measurement calibration device, three-dimensional measurement calibration method, and three-dimensional measurement calibration program

Abstract

A three-dimensional measurement calibration device according to one embodiment of the present invention comprises: a point cloud data acquiring unit for acquiring point cloud data from a plurality of three-dimensional measuring devices that three-dimensionally measure a space to generate the point cloud data; a measured data acquiring unit for acquiring measured data from a sensor that has a resolution higher than the resolution of the plurality of three-dimensional measuring devices and that measures a space to generate the measured data; an external parameter calculating unit for calculating external parameters between the plurality of three-dimensional measuring devices on the basis of the point cloud data and the measured data; and an integrating unit that integrates the point cloud data using the calculated external parameters.

Description

Three-dimensional measurement calibration device, three-dimensional measurement calibration method, and three-dimensional measurement calibration program

[0001] The present invention relates to three-dimensional measurement.

[0002] A laser-based 3D measurement device called LiDAR (Light Detection and Ranging) is known. When multiple LiDARs are used to measure a wide area and construct large-scale 3D data (point cloud data), it is necessary to align the LiDARs, i.e., calibrate them, and calculate extrinsic parameters in order to integrate the measurement results from the multiple LiDARs.

[0003] Yoshisada et al., "Proposal of an Indoor Map Generation Method by Integrating 2D Point Clouds with Different Base Points," IPSJ Technical Report, Vol. 2018-MBL-86, No. 6, pp. 1-12, 2018.

[0004] Increasing the number of LiDARs to expand the measurement range or perform more precise measurements increases the workload for calibration between the LiDARs, and depending on the location of the targets used for calibration, the point cloud data measured by the LiDARs may become sparse, making it impossible to measure the targets correctly and reducing calibration accuracy. Furthermore, increasing the size of the targets to ensure accurate measurement makes them difficult to handle, reducing the efficiency of the calibration work.

[0005] As in the technology disclosed in Non-Patent Document 1, several methods have been proposed for automatically estimating external parameters obtained by calibration, such as by extracting feature points such as walls from point cloud data measured using indoor shapes and performing alignment.

[0006] However, this method is difficult to apply to wide-area spaces with few feature points and at long distances, and is limited to relatively short distances and limited spaces.

[0007] An object of the present invention is to provide a technique for improving the accuracy of calibration in three-dimensional measurement using a plurality of three-dimensional measurement devices.

[0008] A three-dimensional measurement calibration device according to one aspect of the present invention includes a point cloud data acquisition unit that acquires point cloud data from a plurality of three-dimensional measurement devices that measure a space in three dimensions and generate the point cloud data; a measurement data acquisition unit that acquires the measurement data from a sensor that has a higher resolution than that of the plurality of three-dimensional measurement devices and measures the space and generates the measurement data; an external parameter calculation unit that calculates external parameters between the plurality of three-dimensional measurement devices based on the point cloud data and the measurement data; and an integration unit that integrates the point cloud data using the calculated external parameters.

[0009] According to the present invention, a technique for improving the accuracy of calibration in three-dimensional measurement using a plurality of three-dimensional measurement devices is provided.

[0010] Fig. 1 is a block diagram showing the configuration of a three-dimensional measurement system according to an embodiment. Fig. 2 is a diagram for explaining a three-dimensional measurement calibration method according to an embodiment. Fig. 3 is a block diagram showing the hardware configuration of the three-dimensional measurement system according to an embodiment. Fig. 4 is a flowchart showing a three-dimensional measurement calibration method according to an embodiment.

[0011] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0012] Fig. 1 schematically illustrates an example of the configuration of a three-dimensional measurement system 100 according to an embodiment. As illustrated in Fig. 1, the three-dimensional measurement system 100 includes a plurality of LiDARs 110, cameras 120, and a three-dimensional measurement calibration device 130. The LiDARs 110 and the cameras 120 are connected to the three-dimensional measurement calibration device 130. In the example illustrated in Fig. 1, three LiDARs 110, LiDARs 110-1, 110-2, and 110-3, are provided. The number of LiDARs 110 may be two, or may be four or more.

[0013] Each LiDAR 110 uses a laser to measure a space in three dimensions and generate point cloud data. For example, the LiDAR 110 scans a space with a laser beam to generate point cloud data. The point cloud data includes information indicating the three-dimensional shape of the space. Specifically, the point cloud data includes information indicating the three-dimensional positions of multiple points in the space. The three-dimensional positions are expressed in the coordinate system of each LiDAR 110 (e.g., a three-dimensional Cartesian coordinate system). The point cloud data obtained by the LiDAR 110 is sent to the three-dimensional measurement calibration device 130. Hereinafter, the point cloud data obtained by the LiDAR 110 may also be referred to as point cloud data of the LiDAR 110.

[0014] The LiDAR 110 is an example of a three-dimensional measurement device that measures a space in three dimensions and generates point cloud data. Point cloud data is also called three-dimensional data or three-dimensional spatial data. In other embodiments, a three-dimensional measurement device other than the LiDAR may be used.

[0015] The camera 120 is an imaging device that captures an image of a space and generates image data. The image data obtained by the camera 120 is sent to the three-dimensional measurement calibration device 130.

[0016] The camera 120 is an example of a measurement sensor that has a higher resolution than the LiDAR 110 and measures space to generate measurement data. The resolution of the LiDAR 110 corresponds to the number of points whose three-dimensional positions are indicated in the point cloud data. The resolution of the camera 120 corresponds to the number of pixels in the image data. The measurement sensor may be a different type of measurement sensor from the LiDAR 110. A sensor of a different type from the LiDAR 110 is called an alternative sensor. The measurement sensor does not have to be an imaging device that generates image data like the camera 120, as long as it has a higher resolution than the LiDAR 110. In other embodiments, a LiDAR having a higher resolution than the LiDAR 110 may be used as the measurement sensor.

[0017] The LiDARs 110 are placed at different locations. The cameras 120 are placed so that the areas photographed by the cameras 120 overlap with the areas scanned by each LiDAR 110.

[0018] The three-dimensional measurement calibration device 130 receives point cloud data from the LiDAR 110 and receives image data from the camera 120. The three-dimensional measurement calibration device 130 integrates the point cloud data received from the LiDAR 110 using the image data received from the camera 120. The three-dimensional measurement calibration device 130 includes a point cloud data acquisition unit 131, a measurement data acquisition unit 132, an external parameter calculation unit 133, an external parameter storage unit 134, an integration unit 135, and an output unit 136.

[0019] The point cloud data acquisition unit 131 acquires point cloud data using the LiDAR 110. Specifically, the point cloud data acquisition unit 131 causes the LiDAR 110 to perform three-dimensional measurement and receives the point cloud data from the LiDAR 110.

[0020] The measurement data acquisition unit 132 acquires image data using the camera 120. Specifically, the measurement data acquisition unit 132 causes the camera 120 to take an image and receives the image data from the camera 120.

[0021] The external parameter calculation unit 133 calculates external parameters between the LiDAR 110 based on the point cloud data of the LiDAR 110 acquired by the point cloud data acquisition unit 131 and the image data of the camera 120 acquired by the measurement data acquisition unit 132. Specifically, the external parameter calculation unit 133 calculates external parameters between the LiDAR 110 and the camera 120 by performing calibration between the LiDAR 110 and the camera 120 based on the point cloud data of the LiDAR 110 acquired by the point cloud data acquisition unit 131 and the image data of the camera 120 acquired by the measurement data acquisition unit 132. Then, the external parameter calculation unit 133 calculates external parameters between the LiDAR 110 from the calculated external parameters between the LiDAR 110 and the camera 120. In this embodiment, the coordinate system of one of the LiDARs 110 is set as the reference, and the external parameters between the LiDARs 110 include transformation matrices that transform the coordinate systems of the remaining LiDARs 110 into the reference coordinate system. For example, when the coordinate system of LiDAR 110-1 is set as the reference, the external parameters between the LiDARs 110 include a transformation matrix that transforms the coordinate system of LiDAR 110-2 into the coordinate system of LiDAR 110-1, and a transformation matrix that transforms the coordinate system of LiDAR 110-3 into the coordinate system of LiDAR 110-1.

[0022] An example of a method for calculating external parameters between the LiDARs 110 will be described with reference to Fig. 2. In Fig. 2, L1 represents the LiDAR 110-1, L2 represents the LiDAR 110-2, L3 represents the LiDAR 110-3, and C represents the camera 120.

[0023] The external parameter calculation unit 133 performs calibration between the LiDAR 110-1 and the camera 120 based on the point cloud data of the LiDAR 110-1 and the image data of the camera 120. Specifically, the external parameter calculation unit 133 calculates a transformation matrix T that transforms the coordinate system of the LiDAR 110-1 based on the camera 120, based on the point cloud data of the LiDAR 110-1 and the image data of the camera 120. （Ｃ→Ｌ１） In other words, the transformation matrix T （Ｃ→Ｌ１）is a transformation matrix that transforms the coordinate system of the LiDAR 110-1 into the coordinate system of the camera 120. （Ｃ→Ｌ１） corresponds to an external parameter between the LiDAR 110-1 and the camera 120. For example, the external parameter calculation unit 133 identifies the same object included in the area to be scanned by the LiDAR 110-1 and the area to be photographed by the camera 120 based on the point cloud data of the LiDAR 110-1 and the image data of the camera 120, and calculates a transformation matrix T （Ｃ→Ｌ１） The object may be any object, such as a tool present in the city or a target installed for calibration. （Ｃ→Ｌ１） The calculation method is the transformation matrix T （Ｃ→Ｌ１） Any method may be used as long as it is possible to obtain a transformation matrix that transforms the coordinate system into the coordinate system of the reference sensor (the camera 120 in this embodiment).

[0024] Similarly, the external parameter calculation unit 133 calculates a transformation matrix T that transforms the coordinate system of the LiDAR 110-2 into the coordinate system of the camera 120 from the point cloud data of the LiDAR 110-2 and the image data of the camera 120. （Ｃ→Ｌ２） Calculate a transformation matrix T that transforms the coordinate system of the LiDAR 110-3 into the coordinate system of the camera 120 from the point cloud data of the LiDAR 110-3 and the image data of the camera 120. （Ｃ→Ｌ３） Calculate.

[0025] The external parameter calculation unit 133 calculates the transformation matrix T （Ｃ→Ｌ１） and the transformation matrix T （Ｃ→Ｌ２） From this, the transformation matrix T that transforms the coordinate system of LiDAR 110-2 into the coordinate system of LiDAR 110-1 is （Ｌ１→Ｌ２） Specifically, the external parameter calculation unit 133 calculates the transformation matrix T （Ｃ→Ｌ１） The matrix T is the inverse matrix of （Ｌ１→Ｃ） Calculate the matrix T （Ｌ１→Ｃ） and the transformation matrix T （Ｃ→Ｌ２） By calculating the product of （Ｌ１→Ｌ２） The transformation matrix T （Ｌ１→Ｌ２）corresponds to the external parameter between LiDAR 110-1 and LiDAR 110-2. （Ｌ１→Ｌ２） =T （Ｌ１→Ｃ） ×T （Ｃ→Ｌ２） =T （Ｃ→Ｌ１） －１ ×T （Ｃ→Ｌ２）

[0026] Similarly, the external parameter calculation unit 133 calculates the transformation matrix T （Ｃ→Ｌ１） and the transformation matrix T （Ｃ→Ｌ３） From this, the transformation matrix T that transforms the coordinate system of LiDAR 110-3 into the coordinate system of LiDAR 110-1 is （Ｌ１→Ｌ３） Specifically, the external parameter calculation unit 133 calculates the transformation matrix T （Ｃ→Ｌ１） The matrix T is the inverse matrix of （Ｌ１→Ｃ） Calculate the matrix T （Ｌ１→Ｃ） and the transformation matrix T （Ｃ→Ｌ３） By calculating the product of （Ｌ１→Ｌ３） The transformation matrix T （Ｌ１→Ｌ３） corresponds to the external parameter between LiDAR 110-1 and LiDAR 110-3.

[0027] Referring again to FIG. 1, the external parameter storage unit 134 stores the external parameters between the LiDARs 110 obtained by the external parameter calculation unit 133. In this embodiment, the external parameters between the LiDARs 110 are calculated using the transformation matrix T （Ｌ１→Ｌ２） and the transformation matrix T （Ｌ１→Ｌ３） and,

[0028] The integration unit 135 extracts external parameters between the LiDARs 110 from the external parameter storage unit 134, and uses the extracted external parameters to integrate the point cloud data acquired by the point cloud data acquisition unit 131 to generate integrated point cloud data. （Ｌ１→Ｌ２） to the point cloud data of the LiDAR 110-2, thereby obtaining converted point cloud data of the LiDAR 110-2. The converted point cloud data of the LiDAR 110-2 is obtained by converting the point cloud data of the LiDAR 110-2 into the coordinate system of the LiDAR 110-1. Furthermore, the integration unit 135 applies the transformation matrix T （Ｌ１→Ｌ３）to the point cloud data of LiDAR 110-3 to obtain converted point cloud data of LiDAR 110-3. The converted point cloud data of LiDAR 110-3 is obtained by converting the point cloud data of LiDAR 110-3 into the coordinate system of LiDAR 110-1. The integration unit 135 incorporates the converted point cloud data of LiDAR 110-2 and the converted point cloud data of LiDAR 110-3 into the point cloud data of LiDAR 110-1 to obtain integrated point cloud data. The integrated point cloud data includes the point cloud data of LiDAR 110-1, the converted point cloud data of LiDAR 110-2, and the converted point cloud data of LiDAR 110-3.

[0029] The output unit 136 outputs the integrated point cloud data obtained by the integration unit 135. For example, the output unit 136 transmits the integrated point cloud data to an external device (not shown).

[0030] Fig. 3 schematically shows an example of the hardware configuration of the three-dimensional measurement system 100. In Fig. 3, the same components as those shown in Fig. 1 are denoted by the same reference numerals. As shown in Fig. 3, the three-dimensional measurement system 100 includes a plurality of LiDARs 110, a camera 120, and a computer 150. The three-dimensional measurement calibration device 130 shown in Fig. 1 is realized by the computer 150.

[0031] The computer 150 includes a CPU (Central Processing Unit) 151 as a processing circuit, a RAM 152, a storage device 153, and an input / output interface 154. The CPU 151 is connected to the RAM 152, the storage device 153, and the input / output interface 154 so as to be able to communicate with them.

[0032] The CPU 151 is an example of a general-purpose processor capable of executing various programs. The RAM 152 is a volatile memory used by the CPU 151 as a working area. The storage device 153 is a non-volatile memory such as a hard disk drive (HDD) or a solid state drive (SSD). The storage device 153 stores programs executed by the CPU 151, such as a three-dimensional measurement calibration program, and various data. Each program includes multiple computer-executable instructions. The storage device 153 functions as the external parameter storage unit 134 shown in FIG. 1.

[0033] The CPU 151 loads the program stored in the storage device 153 into the RAM 152, and interprets and executes the program. When the three-dimensional measurement calibration program is executed by the CPU 151, it causes the CPU 151 to perform the series of processes described with respect to the three-dimensional measurement calibration device 130 shown in Fig. 1. In other words, the CPU 151 is configured to function as a point cloud data acquisition unit 131, a measurement data acquisition unit 132, an external parameter calculation unit 133, an integration unit 135, and an output unit 136.

[0034] The input / output interface 154 is an interface for connecting an external device. The LiDAR 110 and the camera 120 are connected to the input / output interface 154. The CPU 151 receives point cloud data from the LiDAR 110 via the input / output interface 154, and receives image data from the camera 120 via the input / output interface 154. The CPU 151 may transmit the integrated point cloud data to a data server (not shown) via the input / output interface 154. A display device (not shown) may be connected to the input / output interface 154, and the CPU 151 may display the integrated point cloud data on the display device via the input / output interface 154.

[0035] The hardware configuration shown in Fig. 3 is an example, and a hardware configuration different from the hardware configuration shown in Fig. 3 may be adopted. For example, a dedicated processor such as an FPGA (Field Programmable Gate Array) may be used instead of a general-purpose processor. The processing circuit may include a general-purpose processor, a dedicated processor, or a combination of a general-purpose processor and a dedicated processor.

[0036] The program may be provided to the computer 150 in a state where it is stored on a computer-readable recording medium. In this case, the computer 150 is equipped with a drive that reads data from the recording medium and acquires the program from the recording medium. Examples of recording media include magnetic disks, optical disks (CD-ROM, CD-R, DVD-ROM, DVD-R, etc.), magneto-optical disks (MO, etc.), and semiconductor memories. The program may also be distributed over a network. Specifically, the program may be stored on a server on the network, and the computer 150 may download the program from the server.

[0037] 4 is a schematic diagram showing an example of the procedure of the three-dimensional measurement process according to this embodiment. The process shown in FIG. 4 is executed by the three-dimensional measurement calibration device 130 shown in FIG.

[0038] 4 , the point cloud data acquisition unit 131 acquires point cloud data using the LiDAR 110. For example, the point cloud data acquisition unit 131 drives the LiDAR 110 to scan a space with laser light and receives point cloud data from the LiDAR 110.

[0039] In step S402, the measurement data acquisition unit 132 acquires image data using the camera 120. For example, the measurement data acquisition unit 132 drives the camera 120 to capture an image of the space, and receives the image data from the camera 120.

[0040] Although the process shown in step S402 is shown in FIG. 4 as being performed after the process shown in step S401, typically the process shown in step S402 is performed in parallel with the process shown in step S401.

[0041] In step S403, the external parameter calculation unit 133 performs calibration between the LiDAR 110 and the camera 120 based on the point cloud data obtained in step S401 and the image data obtained in step S402, thereby obtaining external parameters between the LiDAR 110 and the camera 120. Specifically, the external parameter calculation unit 133 calculates a transformation matrix T that transforms the coordinate system of the LiDAR 110-1 into the coordinate system of the camera 120 from the point cloud data of the LiDAR 110-1 and the image data of the camera 120. （Ｃ→Ｌ１） Calculate a transformation matrix T that transforms the coordinate system of the LiDAR 110-2 into the coordinate system of the camera 120 from the point cloud data of the LiDAR 110-2 and the image data of the camera 120. （Ｃ→Ｌ２） Calculate a transformation matrix T that transforms the coordinate system of the LiDAR 110-3 into the coordinate system of the camera 120 from the point cloud data of the LiDAR 110-3 and the image data of the camera 120. （Ｃ→Ｌ３） Calculate.

[0042] In step S404, the external parameter calculation unit 133 calculates the external parameters between the LiDAR 110 from the external parameters between the LiDAR 110 and the camera 120 obtained in step S403. Specifically, the external parameter calculation unit 133 calculates the external parameters between the LiDAR 110 and the camera 120 based on the transformation matrix T （Ｃ→Ｌ１） The matrix T is the inverse matrix of （Ｌ１→Ｃ） Then, the external parameter calculation unit 133 calculates the transformation matrix T （Ｃ→Ｌ２） matrix T （Ｌ１→Ｃ） , thereby transforming the coordinate system of LiDAR 110-2 into the coordinate system of LiDAR 110-1. （Ｌ１→Ｌ２） The external parameter calculation unit 133 obtains the transformation matrix T （Ｃ→Ｌ３） matrix T （Ｌ１→Ｃ） , thereby transforming the coordinate system of LiDAR 110-3 into the coordinate system of LiDAR 110-1. （Ｌ１→Ｌ３） get.

[0043] In step S405, the integration unit 135 integrates the point cloud data obtained in step S401 using the external parameters between the LiDARs 110 obtained in step S404 to generate integrated point cloud data. For example, the integration unit 135 generates the integrated point cloud data by using the transformation matrix T （Ｌ１→Ｌ２） The integration unit 135 transforms the point cloud data of the LiDAR 110-2 using the transformation matrix T （Ｌ１→Ｌ３） The point cloud data of the LiDAR 110-3 is transformed using the point cloud data transformer 110-1, thereby obtaining transformed point cloud data of the LiDAR 110-3. The integrated point cloud data includes the point cloud data of the LiDAR 110-1, the transformed point cloud data of the LiDAR 110-2, and the point cloud data of the LiDAR 110-3.

[0044] In step S406, the output unit 136 outputs the integrated point cloud data obtained by the integration unit 135. For example, the output unit 136 transmits the integrated point cloud data to a data server.

[0045] As described above, the 3D measurement calibration device 130 acquires point cloud data from the LiDAR 110, acquires image data from a camera 120 having a resolution higher than that of the LiDAR 110, calculates external parameters between the LiDAR 110 based on the point cloud data and the image data, and integrates the point cloud data using the calculated external parameters.

[0046] According to the above configuration, image data obtained by the camera 120 with a higher resolution than the LiDAR 110 is used for calibration. This makes it possible to perform calibration at a resolution of a granularity that cannot be measured by the LiDAR 110 alone. As a result, calibration accuracy can be improved. Furthermore, when setting up a calibration target, sufficient calibration accuracy can be obtained even if the target is small. This makes it possible to use a small target, thereby maintaining or improving the efficiency of the calibration work.

[0047] Furthermore, measurement data obtained by a sensor (camera 120 in this embodiment) added for calibration is not incorporated into the integration of point cloud data. In other words, the 3D measurement calibration device 130 integrates only point cloud data obtained by the LiDAR 110. Even if the camera 120 is used as another type of sensor, image data obtained by the camera 120 is used only for calibration, so privacy can be maintained even when using point cloud data obtained by 3D measurement in public spaces, etc.

[0048] For example, the three-dimensional measurement calibration device 130 calculates external parameters between the LiDARs 110 by performing calibration between the LiDAR 110 and the camera 120 based on the point cloud data of the LiDAR 110 and the image data of the camera 120. For example, the LiDAR 110-1 or its coordinate system is set as a reference, and the three-dimensional measurement calibration device 130 calculates a transformation matrix T that transforms the coordinate system of the LiDAR 110-1 into the coordinate system of the camera 120 from the point cloud data of the LiDAR 110-1 and the image data of the camera 120. （Ｃ→Ｌ１） The three-dimensional measurement calibration device 130 calculates a transformation matrix T for transforming the coordinate system of the LiDAR 110-2 into the coordinate system of the camera 120 from the point cloud data of the LiDAR 110-2 and the image data of the camera 120. （Ｃ→Ｌ２） The three-dimensional measurement calibration device 130 calculates a transformation matrix T for transforming the coordinate system of the LiDAR 110-3 into the coordinate system of the camera 120 from the point cloud data of the LiDAR 110-3 and the image data of the camera 120. （Ｃ→Ｌ３） The three-dimensional measurement calibration device 130 calculates the transformation matrix T （Ｃ→Ｌ１） Inverse matrix and transformation matrix T （Ｃ→Ｌ２） From this, the transformation matrix T that transforms the coordinate system of LiDAR 110-2 into the coordinate system of LiDAR 110-1 is （Ｌ１→Ｌ２） The three-dimensional measurement calibration device 130 calculates the transformation matrix T （Ｃ→Ｌ１） Inverse matrix and transformation matrix T （Ｃ→Ｌ３）From this, the transformation matrix T that transforms the coordinate system of LiDAR 110-3 into the coordinate system of LiDAR 110-1 is （Ｌ１→Ｌ３） The external parameters between the LiDARs 110 are calculated using the transformation matrix calculated for the LiDAR 110-2. （Ｃ→Ｌ２） and the transformation matrix calculated for LiDAR 110-3 （Ｃ→Ｌ３） and,

[0049] According to the above configuration, calibration is performed between the LiDAR 110 and the camera 120, and external parameters between the LiDARs 110 are calculated from the external parameters obtained thereby. This eliminates the need for calibration between the LiDARs 110. Because the image data from the camera 120 has a higher resolution than the point cloud data from the LiDAR 110, sufficient calibration accuracy can be obtained even if the object used for calibration is small or located far away.

[0050] The present invention is not limited to the above-described embodiments, and various modifications can be made in the implementation stage without departing from the spirit of the invention. Furthermore, the embodiments may be implemented in appropriate combinations, in which case the combined effects can be obtained. Furthermore, the above-described embodiments include various inventions, and various inventions can be extracted by combining selected components from the disclosed components. For example, if the problem can be solved and the effects can be obtained even if some components are removed from all the components shown in the embodiments, the configuration from which these components are removed can be extracted as an invention.

[0051] DESCRIPTION OF SYMBOLS 100... 3D measurement system 110... LiDAR 120... Camera 130... 3D measurement calibration device 131... Point cloud data acquisition unit 132... Measurement data acquisition unit 133... External parameter calculation unit 134... External parameter storage unit 135... Integration unit 136... Output unit 150... Computer 151... CPU 152... RAM 153... Storage device 154... Input / output interface

Claims

1. A three-dimensional measurement calibration device comprising: a point cloud data acquisition unit that acquires point cloud data from a plurality of three-dimensional measurement devices that measure a space in three dimensions and generate the point cloud data; a measurement data acquisition unit that acquires the measurement data from a sensor that has a higher resolution than the resolution of the plurality of three-dimensional measurement devices and measures the space and generates the measurement data; an external parameter calculation unit that calculates external parameters between the plurality of three-dimensional measurement devices based on the point cloud data and the measurement data; and an integration unit that integrates the point cloud data using the calculated external parameters.

2. The three-dimensional measurement calibration device according to claim 1, wherein the external parameter calculation unit calculates the external parameters between the plurality of three-dimensional measurement devices by performing calibration between the plurality of three-dimensional measurement devices and the sensor based on the point cloud data and the measurement data.

3. The plurality of three-dimensional measuring devices include a first three-dimensional measuring device and one or more second three-dimensional measuring devices, and the external parameter calculation unit calculates a first transformation matrix from the point cloud data and the measurement data of the first three-dimensional measuring device to transform the coordinate system of the first three-dimensional measuring device into the coordinate system of the sensor, calculates a second transformation matrix for each of the one or more second three-dimensional measuring devices from the point cloud data and the measurement data of the second three-dimensional measuring device to transform the coordinate system of the second three-dimensional measuring device into the coordinate system of the sensor, and calculates a third transformation matrix for each of the one or more second three-dimensional measuring devices from the inverse matrix of the first transformation matrix and the second transformation matrix to transform the coordinate system of the second three-dimensional measuring device into the coordinate system of the first three-dimensional measuring device, thereby calculating the external parameters between the plurality of three-dimensional measuring devices, The three-dimensional measurement calibration device according to claim 2 , wherein the external parameters between the plurality of three-dimensional measurement devices include the third transformation matrix calculated for each of the one or more second three-dimensional measurement devices.

4. The three-dimensional measurement calibration device according to claim 1, wherein the integration unit integrates the point cloud data using the calculated external parameters to generate integrated point cloud data, and the integrated point cloud data does not include the measurement data.

5. The three-dimensional measurement calibration device according to claim 1, wherein the plurality of three-dimensional measurement devices are a plurality of LiDAR (Light Detection and Ranging) devices that measure space three-dimensionally using laser light.

6. The three-dimensional measurement calibration device according to claim 1, wherein the sensor is a camera that captures an image of a space and generates image data.

7. A computer-executed three-dimensional measurement calibration method comprising: acquiring point cloud data from a plurality of three-dimensional measurement devices that measure a space in three dimensions and generate the point cloud data; acquiring the measurement data from a sensor that has a higher resolution than the plurality of three-dimensional measurement devices and measures the space and generates the measurement data; calculating external parameters between the plurality of three-dimensional measurement devices based on the point cloud data and the measurement data; and integrating the point cloud data using the calculated external parameters.

8. A three-dimensional measurement calibration program for causing a computer to function as each unit of the three-dimensional measurement calibration device according to claim 1.

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