JP7412651B1 - Point cloud joining device, point cloud joining method, point cloud joining program, and measurement vehicle - Google Patents

Abstract

A tie point extraction unit (112) extracts tie points from each of the reference laser point group included in the reference measurement data and the target laser point group included in the target measurement data. A self-position and orientation correction unit (113) corrects self-position and orientation data included in the target measurement data so that the positions of the tie points match. A three-dimensional conversion unit (114) generates a corrected laser point group using the corrected self-position and orientation data. At least one of the reference laser point group and the target laser point group includes an oblique laser point group obtained using an oblique laser scanner and a horizontal laser point group obtained using a horizontal laser scanner.

Description

The present disclosure relates to a technique for merging multiple point clouds obtained with a mobile mapping system (MMS).

In the world of MMS, a trend is toward technology that consistently combines multiple point clouds obtained through multiple runs.
On roads with wide lane widths or interchanges, it is not possible to acquire a point cloud for the entire area in one drive. In such cases, by using the automatic joining technique as described above, a wider range of point clouds can be used. For example, when obtaining a point cloud for the entire area of a highway, automatic joining technology is required.

Automated splicing is a key technology for MMS as the demand for highway and road point clouds increases.

Patent Document 1 discloses acquiring measurement data using a measurement vehicle, generating a three-dimensional point group using the measurement data, and joining a plurality of three-dimensional point groups.

Japanese Patent Application Publication No. 2021-135380

Conventional automatic joining software extracts features (such as telephone poles and white lines) from a point cloud obtained by measuring diagonally with a laser, and matches the features between the point clouds.
For this reason, if features cannot be extracted, the point clouds cannot be joined together. Therefore, automatic joining will not work on roads with long distances between utility poles or roads with unbroken white lines.

The present disclosure aims to prevent automatic joining from malfunctioning due to inability to extract features.

The point group joining device of the present disclosure includes:
One or more feature points in the reference laser point group, which is a three-dimensional point group included in the reference measurement data, and one or more features in the target laser point group, which is a three-dimensional point group included in the target measurement data. a tie point extraction unit that extracts common feature points from each of the points as tie points;
a self-position and orientation correction unit that corrects self-position and orientation data included in the target measurement data so that the position of the tie point of the reference laser point group matches the position of the tie point of the target laser point group;
and a three-dimensionalization unit that generates a corrected laser point group that is a three-dimensional point group using the corrected self-position and orientation data.
At least one of the reference measurement data and the target measurement data is measurement data obtained using a special measurement vehicle equipped with an oblique laser scanner and a horizontal laser scanner.
At least one of the reference laser point group and the target laser point group is a three-dimensional point group obtained using the oblique laser scanner, and a three-dimensional point group obtained using the horizontal laser scanner. and a horizontal laser point cloud.

According to the present disclosure, a horizontal laser point group obtained using a horizontal laser scanner is used in addition to an oblique laser point group obtained using an oblique laser scanner. This makes it possible to prevent automatic joining from failing due to inability to extract features.

1 is a configuration diagram of a point group joining device 100 in Embodiment 1. FIG. FIG. 3 is a configuration diagram of storage unit 120 in Embodiment 1. FIG. 4 is a configuration diagram of measurement vehicle 400 in Embodiment 1. 1 is a flowchart of a point group joining method in Embodiment 1. FIG. 3 is a configuration diagram of target measurement data 301 generated by the point cloud joining method in the first embodiment. FIG. 3 is a diagram illustrating the alignment of two point clouds in automatic joining. FIG. 4 is a diagram showing an effective range of an oblique laser scanner 421. FIG. 4 is a diagram showing the respective effective ranges of a diagonal laser scanner 421 and a horizontal laser scanner 422. A diagram showing the relationship between laser measurement and median strip. A diagram showing an example of a horizontal laser point group. A diagram showing an example of a horizontal laser point group. A diagram showing an example of a horizontal laser point group. A diagram showing an example of a horizontal laser point group. The figure which shows the reference point group, the target point group, and the target point group (3 patterns) after joining. 1 is a hardware configuration diagram of a point cloud joining device 100 in Embodiment 1. FIG.

In the embodiments and drawings, the same or corresponding elements are denoted by the same reference numerals. Descriptions of elements assigned the same reference numerals as explained elements will be omitted or simplified as appropriate. Arrows in the figure mainly indicate the flow of data or processing.

Embodiment 1.
The point group joining device 100 will be explained based on FIGS. 1 to 10.

***Explanation of configuration***
The configuration of the point group joining device 100 will be explained based on FIG. 1.
The point cloud joining device 100 is a computer that includes hardware such as a processor 101, a memory 102, an auxiliary storage device 103, a communication device 104, and an input/output interface 105. These pieces of hardware are connected to each other via signal lines.

The processor 101 is an IC that performs arithmetic processing and controls other hardware. For example, processor 101 is a CPU.
IC is an abbreviation for Integrated Circuit.
CPU is an abbreviation for Central Processing Unit.

Memory 102 is a volatile or non-volatile storage device. Memory 102 is also called main storage or main memory. For example, memory 102 is a RAM. The data stored in memory 102 is saved in auxiliary storage device 103 as needed.
RAM is an abbreviation for Random Access Memory.

The auxiliary storage device 103 is a nonvolatile storage device. For example, the auxiliary storage device 103 is a ROM, an HDD, a flash memory, or a combination thereof. Data stored in the auxiliary storage device 103 is loaded into the memory 102 as needed.
ROM is an abbreviation for Read Only Memory.
HDD is an abbreviation for Hard Disk Drive.

Communication device 104 is a receiver and transmitter. For example, communication device 104 is a communication chip or NIC. Communication between the point group joining device 100 is performed using a communication device 104.
NIC is an abbreviation for Network Interface Card.

The input/output interface 105 is a port to which an input device and an output device are connected. For example, the input/output interface 105 is a USB terminal, the input device is a keyboard and a mouse, and the output device is a display. Input/output of the point group joining device 100 is performed using an input/output interface 105.
USB is an abbreviation for Universal Serial Bus.

The point cloud joining device 100 includes elements such as a feature point extraction section 111, a tie point extraction section 112, a self-position and orientation correction section 113, a three-dimensionalization section 114, a three-dimensionalization accuracy prediction section 115, and a camera position and orientation calculation section 116. . These elements are implemented in software.

The auxiliary storage device 103 includes computers that function as a feature point extraction section 111 , a tie point extraction section 112 , a self-position/orientation correction section 113 , a three-dimensionalization section 114 , a three-dimensionalization accuracy prediction section 115 , and a camera position/orientation calculation section 116 . A point cloud joining program is stored in the memory. The point cloud joining program is loaded into memory 102 and executed by processor 101.
The auxiliary storage device 103 further stores an OS. At least a portion of the OS is loaded into memory 102 and executed by processor 101.
The processor 101 executes the point cloud joining program while executing the OS.
OS is an abbreviation for Operating System.

Input/output data of the point group joining program is stored in the storage unit 120.
Auxiliary storage device 103 functions as storage section 120. However, a storage device such as the memory 102, a register in the processor 101, and a cache memory in the processor 101 may function as the storage unit 120 instead of or together with the auxiliary storage device 103.

The point cloud joining program can be recorded (stored) in a computer-readable manner on a non-volatile recording medium such as an optical disk or a flash memory.

The configuration of the storage unit 120 will be explained based on FIG. 2.
The storage unit 120 stores reference measurement data 200 and target measurement data 300.
The reference measurement data 200 includes self-position and orientation data 210, distance and orientation information 220, a reference laser point group 230, image data 240, and camera position and orientation data 250. The reference laser point group 230 includes an oblique laser point group 231 and a horizontal laser point group 232 .
The target measurement data 300 includes self-position and orientation data 310, distance/azimuth information 320, target laser point group 330, and image data 340. The target laser point group 330 includes an oblique laser point group 331 and a horizontal laser point group 332.

The reference measurement data 200 is measurement data that serves as a reference, and the target measurement data 300 is measurement data to be processed.
Measurement data is obtained using a measurement vehicle.
The measurement vehicle is a vehicle equipped with various sensors and is used in a mobile mapping system (MMS).

The reference measurement data 200 and the target measurement data 300 are obtained using the measurement vehicle 400.
The measurement vehicle 400 is a measurement vehicle (special measurement vehicle) in which an oblique laser scanner 421 and a horizontal laser scanner 422 are installed. The measurement vehicle is also referred to as a mobile measurement device.

The configuration of measurement vehicle 400 will be explained based on FIG. 3.
A position and orientation measuring device is installed in the measurement vehicle 400. Specifically, a receiver 411 and an IMU 412 are installed in the measurement vehicle 400.
The receiver 411 performs satellite positioning by receiving positioning signals transmitted from each of a plurality of positioning satellites.
IMU 412 is an inertial measurement device and measures angular velocity and acceleration.
Using the received data obtained by receiver 411 and the inertial measurement data obtained by IMU 412, the position and orientation of measurement vehicle 400 at each time is calculated.
Data indicating the position and orientation of measurement vehicle 400 at each time is referred to as self-position and orientation data.
Self-position and orientation data included in the reference measurement data 200 is referred to as self-position and orientation data 210.
The self-position and orientation data included in the target measurement data 300 is referred to as self-position and orientation data 310.

A diagonal laser scanner 421 and a horizontal laser scanner 422 are installed in the measurement vehicle 400.
The diagonal laser scanner 421 is a laser scanner that is installed facing in the diagonal direction and measures the distance and direction to each measurement point located in the diagonal direction.
The horizontal laser scanner 422 is a laser scanner that is installed facing the horizontal direction and measures the distance and direction to each measurement point located in the horizontal direction.
A measurement point is a point measured by a laser scanner. Each point on the surface of the feature that exists in the measurement direction becomes a measurement point. Specific examples of features are roads, utility poles, and walls.
Data indicating the distance and direction from the laser scanner to each measurement point is referred to as distance and direction information.

Three-dimensional coordinate values of each measurement point are calculated using the self-position and orientation data and distance and orientation information.
Data indicating the three-dimensional coordinate values of each measurement point is referred to as a three-dimensional point group. A 3D point cloud consists of multiple 3D points. The three-dimensional point indicates the three-dimensional coordinate value of the measurement point.
The three-dimensional point group included in the reference measurement data 200 is referred to as a reference laser point group 230. The reference laser point group 230 is generated using the self-position/orientation data 210 and the distance/azimuth information 220.
A three-dimensional point group generated using the distance/azimuth information 220 of the oblique laser scanner 421 is referred to as an oblique laser point group 231. A three-dimensional point group generated using the distance/azimuth information 220 of the horizontal laser scanner 422 is referred to as a horizontal laser point group 232.
The three-dimensional point group included in the target measurement data 300 is referred to as a target laser point group 330. The target laser point group 330 is generated using the self-position/orientation data 310 and the distance/azimuth information 320.
A three-dimensional point group generated using the distance/azimuth information 320 of the oblique laser scanner 421 is referred to as an oblique laser point group 331. A three-dimensional point group generated using the distance/azimuth information 320 of the horizontal laser scanner 422 is referred to as a horizontal laser point group 332.

A camera is installed in the measurement vehicle 400. For example, a camera 431 and an all-around camera 432 are installed in the measurement vehicle 400.
The camera 431 is a camera that photographs the front.
The all-around camera 432 is a camera that takes pictures of all the surroundings.
Images are obtained by taking pictures using a camera. Data including images at each time is referred to as image data.
The image data included in the reference measurement data 200 is referred to as image data 240.
Image data included in the target measurement data 300 is referred to as image data 340.

The position and orientation of the camera at each time is calculated using the self-position and orientation data.
Data indicating the position and orientation of the camera at each time is referred to as camera position and orientation data.
The camera position and orientation data included in the reference measurement data 200 is referred to as camera position and orientation data 250. Camera position and orientation data 250 is generated using self-position and orientation data 210.

***Operation explanation***
The operation procedure of the point group joining device 100 corresponds to a point group joining method. Further, the operation procedure of the point cloud joining device 100 corresponds to the processing procedure by the point cloud joining program.

An overview of the point group joining method will be explained based on FIG. 4.
The point cloud joining device 100 uses the reference measurement data 200 and the target measurement data 300 to generate target measurement data 301.
The target measurement data 301 is measurement data corresponding to the target measurement data 300.

The structure of the target measurement data 301 will be explained based on FIG. 5.
The target measurement data 301 includes self-position and orientation data 311, distance and orientation information 320, target laser point group 390, image data 340, camera position and orientation data 350, and prediction accuracy 360.
Self-position and orientation data 311 is corrected self-position and orientation data 310.
The target laser point group 390 is a three-dimensional point group generated using the self-position/orientation data 311 and the distance/azimuth information 320.
Camera position and orientation data 350 is camera position and orientation data generated using self-position and orientation data 311.
The prediction accuracy 360 is data indicating the prediction accuracy of the target laser point group 390.

Returning to FIG. 4, details of the point group joining method will be explained.
In step S110, the feature point extraction unit 111 extracts one or more feature points from the reference laser point group 230.
Specifically, the feature point extraction unit 111 extracts one or more feature points from each of the diagonal laser point group 231 and the horizontal laser point group 232.

Further, the feature point extraction unit 111 extracts one or more feature points from the target laser point group 330.
Specifically, the feature point extraction unit 111 extracts one or more feature points from each of the diagonal laser point group 331 and the horizontal laser point group 332.

For example, a laser point on a utility pole or wall (particularly at a corner) becomes a feature point.

In step S120, the tie point extraction unit 112 extracts common feature points from each of one or more feature points in the reference laser point group 230 and feature points in the target laser point group 330. The extracted feature points are called tie points.

In particular, the tie point extraction unit 112 extracts feature points with high reliability as common feature points as tie points.

In step S130, the self-position and orientation correction unit 113 corrects the self-position and orientation data 310 so that the positions of the tie points of the reference laser point group 230 and the tie points of the target laser point group 330 match.

Self-position and orientation data 310 is corrected as follows.
First, the self-position and orientation correction unit 113 calculates the difference between the tie point position of the reference laser point group 230 and the tie point position of the target laser point group 330 as an error.
Then, the self-position and orientation correction unit 113 corrects each position and orientation shown in the self-position and orientation data 310 according to the error.

In step S130, the self-position and orientation data 310 is corrected to generate self-position and orientation data 311.

In step S140, the three-dimensionalization unit 114 uses the self-position and orientation data 311 and the distance and orientation information 320 to generate a target laser point group 390.
The target laser point group 390 shows a plurality of three-dimensional points corresponding to a plurality of measurement points. The target laser point group 390 is the target laser point group 330 after correction.
The three-dimensional point indicates the three-dimensional coordinate value of the measurement point.
The measurement point is specified as a point distant from the self-position and orientation at the time of measurement in the distance and direction indicated by the target laser point.

In step S150, the three-dimensional accuracy prediction unit 115 predicts the accuracy of the target laser point group 390 and calculates the prediction accuracy 360.

Prediction accuracy 360 is calculated as follows.
First, the feature point extraction unit 111 extracts one or more feature points from each of the reference laser point group 230 and the target laser point group 390.
Next, the tie point extraction unit 112 extracts tie points from each of the one or more feature points in the reference laser point group 230 and the one or more feature points in the target laser point group 390.
Next, the three-dimensional accuracy prediction unit 115 calculates the difference between the position of the tie point of the reference laser point group 230 and the position of the tie point of the target laser point group 390 as an error.
The three-dimensional accuracy prediction unit 115 then calculates the prediction accuracy 360 using the error. The larger the error, the lower the prediction accuracy 360, and the smaller the error, the higher the prediction accuracy 360.

In step S160, the camera position and orientation calculation unit 116 uses the self-position and orientation data 311 to generate camera position and orientation data 350.
Specifically, camera position/orientation calculation unit 116 calculates the position/orientation of the camera at each time by shifting the relative position/orientation of the camera with respect to measurement vehicle 400 from its own position/orientation at each time.

***Effects of Embodiment 1***
Embodiment 1 is characterized in that feature extraction and automatic joining are performed using a horizontally installed laser. That is, in the first embodiment, a horizontal laser is installed to improve the performance of automatic bonding.
Horizontal lasers, because they are mounted horizontally, can capture features much further away than lasers mounted at an angle.
This makes it possible to capture feature points such as distant telephone poles that could not be captured normally, improving the performance of automatic joining. Automatic joining is especially effective for utility poles, and the ability to capture distant utility poles is an advantage.
Embodiment 1 makes it possible to use distant features, distant walls, etc., and reduces the number of sections where point clouds cannot be joined because tie points cannot be measured. Furthermore, improvement in bonding accuracy can be expected.

***Example of Embodiment 1***
The horizontal laser scanner 422 is highly effective if it is used both during the measurement in which the reference measurement data 200 is obtained (reference measurement) and in the measurement in which the target measurement data 300 is obtained (object measurement).
However, the horizontal laser scanner 422 is effective even if it is used only for reference measurement or target measurement.
That is, the reference measurement is performed using a measurement vehicle (normal measurement vehicle) in which the horizontal laser scanner 422 is not installed, and the reference laser point group 230 does not need to include the horizontal laser point group 232. In this case, the target measurement is performed using a measurement vehicle (special measurement vehicle) installed with the horizontal laser scanner 422, and the target laser point group 330 includes the horizontal laser point group 332.
Further, the target measurement may be performed using a normal measurement vehicle, and the target laser point group 330 may not include the horizontal laser point group 332. In this case, the reference measurement is performed using a special measurement vehicle, and the reference laser point group 230 includes the horizontal laser point group 232 .

It is desirable to use a laser scanner with a long measurement distance (laser flight distance) as the horizontal laser scanner 422. However, the distance accuracy of the horizontal laser scanner 422 is not so important.

***Supplement to Embodiment 1***
FIG. 6 shows how two point clouds are aligned by automatic joining.
Automatic joining is a technique that aligns the point clouds obtained from two completely different measurements at different locations and combines them into one point cloud.
Automatic joining uses multiple point clouds generated using different location information to reshape one point cloud to match another point cloud.
Automatic joining extracts feature points from each point group and performs tie point matching. For example, automatic joining is performed when a vehicle drives in different lanes and measurements are taken for each lane in a situation where the satellite is not visible.
Automatic joining is performed by extracting feature points from a point cloud and performing tie point matching. Therefore, automatic joining has the problem of not being able to match point clouds of roads with few feature points.

FIG. 7 shows how a utility pole is measured using the oblique laser scanner 421.
In a normal MMS, a laser is installed obliquely, such as in an oblique laser scanner 421. This is because the measurement vehicle 400 travels and the laser irradiates various locations with laser light. The space is then constructed using a cloud of laser points.
Here, it is assumed that the oblique laser scanner 421 is installed at an inclination of 45 degrees and that the distance range of the oblique laser scanner 421 is 100 meters. In this case, the laser beam hits the ground behind you, and you can only measure a few meters behind you at most. It is assumed that a utility pole exists diagonally in front of measurement vehicle 400. The height of the telephone pole is only about 16 meters. Therefore, the effective range of the oblique laser scanner 421 in the horizontal direction is only about 16 meters.
On the other hand, when the horizontal laser scanner 422 is used, the distance range of the horizontal laser scanner 422 itself becomes the effective range in the horizontal direction. In other words, if the distance range of the horizontal laser scanner 422 is 100 meters, the effective range is ±100 meters in the horizontal direction.

FIG. 8 shows the effective ranges of the oblique laser scanner 421 and the horizontal laser scanner 422 in the horizontal direction.
The darkly shaded area indicates the effective range of the diagonal laser scanner 421, and the entire shaded area indicates the effective range of the horizontal laser scanner 422. The white circles represent telephone poles.
The horizontal laser scanner 422 can capture targets (such as utility poles) over a much wider range than the oblique laser scanner 421.
In FIG. 8, the diagonal laser scanner 421 captures two utility poles, while the horizontal laser scanner 422 captures eight utility poles.
Of course, even if the horizontal laser scanner 422 is used, the effective range is not always ±100 meters, and if there is a building or other obstruction, the effective range will be narrowed.

FIG. 9 shows the relationship between laser measurement and median strip.
The diagonal dashed line represents laser light from the diagonal laser. The horizontal dash-dotted line represents laser light from a horizontal laser.
If there is a median strip, the laser light from the diagonal laser is blocked, so it is not possible to use the diagonal laser to obtain a point group on the opposite lane across the median strip. On the other hand, even if the horizontal laser is installed at the same height as the diagonal laser, the laser light from the horizontal laser is not blocked, so it is difficult to use the horizontal laser to acquire a point cloud from a distance across the median strip. Can be done. Thus, the effectiveness of horizontal lasers is high.

Since the ground is not irradiated with the laser light from the horizontal laser scanner 422, the height of the measurement point is unknown even if the horizontal laser point is referred to. Therefore, the height of the self-position cannot be corrected using only the horizontal laser point group.
However, the laser beam from the oblique laser scanner 421 is irradiated onto the ground. Since the ground is used as a reference for height correction, it is possible to correct the height of one's own position using the oblique laser point group.

FIG. 10 shows an example of a horizontal laser point cloud. Two parallel solid lines represent the travel route of measurement vehicle 400.
By using the horizontal laser scanner 422, a cross-section is obtained at the height at which the horizontal laser scanner 422 is installed. By using the horizontal laser scanner 422, distant data can be acquired.

FIG. 11 shows an example of a horizontal laser point group on a utility pole. The cross mark indicates the center of the detected utility pole.
Point cloud (1) is a point cloud obtained in a very favorable measurement environment.
Point cloud (2) is a point cloud obtained in a reasonably good measurement environment.

FIG. 12 shows an example of a horizontal laser point group on a wall surface. The white straight line represents the detected straight wall surface.
FIG. 13 shows an example of a horizontal laser point group on a wall surface. The cross mark indicates the detected wall angle.

FIG. 14 shows five types of point clouds.
Point group (1) is a reference point group. Point group (2) is a target point group before joining. Point group (3) is a target point group after bonding using an oblique laser. Point group (4) is the target point group after bonding using a horizontal laser. Point group (5) is a target point group after joining using an oblique laser and a horizontal laser.
Point group (5) and point group (4) are closer to point group (1) than point group (3). Therefore, it can be said that the use of a horizontal laser is effective.

The hardware configuration of the point group joining device 100 will be explained based on FIG. 15.
The point group joining device 100 includes a processing circuit 109 .
The processing circuit 109 is hardware that realizes the feature point extraction section 111, the tie point extraction section 112, the self-position and orientation correction section 113, the three-dimensionalization section 114, the three-dimensionalization accuracy prediction section 115, and the camera position and orientation calculation section 116. be.
The processing circuit 109 may be dedicated hardware or may be the processor 101 that executes a program stored in the memory 102.

When processing circuit 109 is dedicated hardware, processing circuit 109 is, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC, an FPGA, or a combination thereof.
ASIC is an abbreviation for Application Specific Integrated Circuit.
FPGA is an abbreviation for Field Programmable Gate Array.

The point group joining device 100 may include a plurality of processing circuits that replace the processing circuit 109.

In the processing circuit 109, some functions may be realized by dedicated hardware, and the remaining functions may be realized by software or firmware.

In this way, the functions of the point cloud joining device 100 can be realized by hardware, software, firmware, or a combination thereof.

Embodiment 1 is an illustration of a preferred embodiment and is not intended to limit the technical scope of the present disclosure. Embodiment 1 may be implemented partially or in combination with other embodiments. The procedures described using flowcharts and the like may be modified as appropriate.

The "section" of each element of the point group joining device 100 may be read as "process", "process", "circuit", or "circuitry".

100 point cloud joining device, 101 processor, 102 memory, 103 auxiliary storage device, 104 communication device, 105 input/output interface, 109 processing circuit, 111 feature point extraction unit, 112 tie point extraction unit, 113 self-position and orientation correction unit, 114 3-dimensional conversion unit, 115 3-dimensional accuracy prediction unit, 116 camera position and orientation calculation unit, 120 storage unit, 200 reference measurement data, 210 self-position and orientation data, 220 distance and orientation information, 230 reference laser point group, 231 oblique laser point group, 232 horizontal laser point group, 240 image data, 250 camera position and orientation data, 300 target measurement data, 301 target measurement data, 310 self-position and orientation data, 311 self-position and orientation data, 320 distance and orientation information, 330 target laser point group , 331 Oblique laser point group, 332 Horizontal laser point group, 340 Image data, 350 Camera position and orientation data, 360 Prediction accuracy, 390 Target laser point group, 391 Oblique laser point group, 392 Horizontal laser point group, 400 Measurement vehicle, 411 Receiver, 412 IMU, 421 Oblique laser scanner, 422 Horizontal laser scanner, 431 Camera, 432 All-around camera.

Claims (8)

Generate a correction laser point group that is a three-dimensional point group that matches the reference laser point group that is a three-dimensional point group included in the reference measurement data obtained in the reference measurement and is combined with the reference laser point group. , a point group joining device for joining the correction laser point group to the reference laser point group,
One or more feature points in the reference laser point group and one in the target laser point group that is a three-dimensional point group included in target measurement data obtained by target measurement performed separately from the reference measurement. a tie point extraction unit that extracts a common feature point as a tie point from each of the three or more feature points;
The target measurement is performed separately from the reference measurement so that the three-dimensional coordinate value of the tie point of the reference laser point group matches the three-dimensional coordinate value of the tie point of the target laser point group. a self-position and orientation correction unit that corrects self-position and orientation data included in the target measurement data;
Distance/azimuth information included in the corrected self-position/orientation data and the target measurement data obtained in the target measurement performed separately from the reference measurement and indicating a plurality of distance/azimuths with respect to a plurality of measurement points at the time of the target measurement. a three-dimensionalization unit that generates a three-dimensional point group to be joined with the three-dimensional point group included in the reference measurement data as the corrected laser point group using the three-dimensional point group;
Equipped with
Each of the reference measurement data and the target measurement data is measurement data obtained using a measurement vehicle equipped with an oblique laser scanner,
At least one of the reference measurement data and the target measurement data is measurement data obtained using a special measurement vehicle equipped with an oblique laser scanner and a horizontal laser scanner,
Each of the reference laser point group and the target laser point group includes an oblique laser point group that is a three-dimensional point group obtained using the oblique laser scanner,
At least one of the reference laser point group and the target laser point group includes the oblique laser point group and a horizontal laser point group that is a three-dimensional point group obtained using the horizontal laser scanner, and including a laser point cloud and the tie point extracted using the horizontal laser point cloud ,
The horizontal laser scanner is installed at the same height as the diagonal laser scanner or at a higher position than the diagonal laser scanner,
The horizontal laser point group includes three-dimensional points at points that are not measured by the oblique laser scanner but are measured by the horizontal laser scanner. The point group joining device includes a feature point extraction unit that extracts the one or more feature points from the reference laser point group and extracts the one or more feature points from the target laser point group,
The tie point extraction unit extracts the tie point from each of the one or more feature points extracted from the reference laser point group and the one or more feature points extracted from the target laser point group. ,
At least one of the tie points is extracted using the diagonal laser point cloud and the horizontal laser point cloud.
The point group joining device according to claim 1. Both the reference measurement data and the target measurement data are data obtained using the special measurement vehicle,
The point group joining device according to claim 1 or 2, wherein both the reference laser point group and the target laser point group include the oblique laser point group and the horizontal laser point group. The normal measurement vehicle is a measurement vehicle in which the diagonal laser scanner is installed and the horizontal laser scanner is not installed,
The reference measurement data is data obtained using the normal measurement vehicle,
the reference laser point group includes the oblique laser point group but does not include the horizontal laser point group,
The target measurement data is data obtained using the special measurement vehicle,
The point group joining device according to claim 1 or 2, wherein the target laser point group includes the oblique laser point group and the horizontal laser point group. The normal measurement vehicle is a measurement vehicle in which the diagonal laser scanner is installed and the horizontal laser scanner is not installed,
The reference measurement data is data obtained using the special measurement vehicle,
The reference laser point group includes the oblique laser point group and the horizontal laser point group,
The target measurement data is data obtained using the normal measurement vehicle,
3. The point group joining device according to claim 1, wherein the target laser point group includes the oblique laser point group and does not include the horizontal laser point group. Generate a correction laser point group that is a three-dimensional point group that matches the reference laser point group that is a three-dimensional point group included in the reference measurement data obtained in the reference measurement and is combined with the reference laser point group. , a point group joining method of joining the correction laser point group to the reference laser point group,
The point cloud joining device
One or more feature points in the reference laser point group and one in the target laser point group that is a three-dimensional point group included in target measurement data obtained by target measurement performed separately from the reference measurement. Extract common feature points from each of the three or more feature points as tie points,
The target measurement is performed separately from the reference measurement so that the three-dimensional coordinate value of the tie point of the reference laser point group matches the three-dimensional coordinate value of the tie point of the target laser point group. correcting self-position and orientation data included in the target measurement data,
Distance/azimuth information included in the corrected self-position/orientation data and the target measurement data obtained in the target measurement performed separately from the reference measurement and indicating a plurality of distance/azimuths with respect to a plurality of measurement points at the time of the target measurement. to generate a three-dimensional point group to be joined with the three-dimensional point group included in the reference measurement data as the corrected laser point group ,
Each of the reference measurement data and the target measurement data is measurement data obtained using a measurement vehicle equipped with an oblique laser scanner,
At least one of the reference measurement data and the target measurement data is measurement data obtained using a special measurement vehicle equipped with an oblique laser scanner and a horizontal laser scanner,
Each of the reference laser point group and the target laser point group includes an oblique laser point group that is a three-dimensional point group obtained using the oblique laser scanner,
At least one of the reference laser point group and the target laser point group includes the oblique laser point group and a horizontal laser point group that is a three-dimensional point group obtained using the horizontal laser scanner,
The extraction of the tie points includes tie points extracted using the diagonal laser point group and the horizontal laser point group,
The horizontal laser scanner is installed at the same height as the diagonal laser scanner or at a higher position than the diagonal laser scanner,
The horizontal laser point group includes three-dimensional points at points that are not measured by the oblique laser scanner but are measured by the horizontal laser scanner. Generate a correction laser point group that is a three-dimensional point group that matches the reference laser point group that is a three-dimensional point group included in the reference measurement data obtained in the reference measurement and is combined with the reference laser point group. for,
One or more feature points in the reference laser point group and one in the target laser point group that is a three-dimensional point group included in target measurement data obtained by target measurement performed separately from the reference measurement. tie point extraction processing for extracting common feature points as tie points from each of the three or more feature points;
The target measurement is performed separately from the reference measurement so that the three-dimensional coordinate value of the tie point of the reference laser point group matches the three-dimensional coordinate value of the tie point of the target laser point group. self-position and orientation correction processing that corrects self-position and orientation data included in the target measurement data;
Distance/azimuth information included in the corrected self-position/orientation data and the target measurement data obtained in the target measurement performed separately from the reference measurement and indicating a plurality of distance/azimuths with respect to a plurality of measurement points at the time of the target measurement. a three-dimensional process of generating a three-dimensional point group to be joined with the three-dimensional point group included in the reference measurement data as the corrected laser point group using the three-dimensional point group;
It is a point cloud joining program that allows a computer to execute
Each of the reference measurement data and the target measurement data is measurement data obtained using a measurement vehicle equipped with an oblique laser scanner,
At least one of the reference measurement data and the target measurement data is measurement data obtained using a special measurement vehicle equipped with an oblique laser scanner and a horizontal laser scanner,
Each of the reference laser point group and the target laser point group includes an oblique laser point group that is a three-dimensional point group obtained using the oblique laser scanner,
At least one of the reference laser point group and the target laser point group includes the oblique laser point group and a horizontal laser point group that is three-dimensional point group data obtained using the horizontal laser scanner,
The tie point extraction process includes tie points extracted using the oblique laser point group and the horizontal laser point group,
The horizontal laser scanner is installed at the same height as the diagonal laser scanner or at a higher position than the diagonal laser scanner,
The horizontal laser point group includes three-dimensional points at points that are not measured by the oblique laser scanner but are measured by the horizontal laser scanner. A measurement vehicle used as the special measurement vehicle in the point cloud joining method according to claim 6 ,
the oblique laser scanner;
the horizontal laser scanner;
Equipped with
The horizontal laser scanner is a measurement vehicle installed at the same height as the diagonal laser scanner or at a higher position than the diagonal laser scanner.

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