JP2013040886A - Method and program for measuring three-dimensional point group - Google Patents

Abstract

PROBLEM TO BE SOLVED: To solve the problem in which: stationary measurement can provide an average solution by remaining at rest for a long time to decrease the degradation in accuracy, but unlike stationary measurement mobile measurement cannot decrease the degradation in accuracy by time averaging because a measurement vehicle for mobile measurement generally cannot rest or move at a low speed, since positioning error factors originally owned by a GPS satellite cause degradation in position-fix accuracy even when a Fix solution is obtained by GPS positioning operation.SOLUTION: While a measurement vehicle drives on the same route a plurality of times, a fixed object that remains unchanged in position in the result of a laser point group during each drive is let to be a reference point. Laser point groups are expanded and/or contracted so that reference points coincide with one another. Then each point is weighed with reliability of its position accuracy and the average result is regarded as a true value. In addition, conditions of GPS satellites keep changing during the drive, position correction amounts are calculated for each drive by specifying a fixed object at fixed intervals, not calculated with one object.

Description

  The present invention includes, for example, a three-dimensional point group around a mobile measurement vehicle, which is mounted by a mobile measurement vehicle equipped with a laser scanner and a camera, and a combination of GPS (Global Positioning System) and a gyro (IMU: Internal Measurement Unit). 3D point cloud measurement method for generating highly accurate 3D point cloud data by post-processing using the above data.

2. Description of the Related Art In recent years, a mobile body measuring vehicle that has a laser scanner and a camera mounted on a vehicle and collects position information around the vehicle has been developed (see, for example, Patent Documents 1 and 2).

When acquiring position information of a building or the like on the ground by mounting a laser scanner on an aircraft or the like, a method using GCP (Grand Control Point) is known as a method for improving the accuracy of the position.
The method using GCP is a method of using a known coordinate point such as a precision survey or a reference point in advance to find a corresponding point in measured data and correcting the deviation amount. As a method of correcting the shift amount, in a simple case, it is performed by dividing a certain section and moving the point group in parallel, and in a more complicated case, it is performed by performing a trapezoidal conversion or the like (see, for example, Patent Document 3). .
In the method using GCP, accuracy increases as the number of GCP points increases, but there is a problem that it takes time and cost because GCP point measurement uses static surveying or the like.

International Publication No. 2010/024212 Pamphlet JP 2010-175423 A JP 2006-189372 A

  When positioning the position of 3D point cloud data collected by a measurement vehicle that combines a GPS and a gyro with a laser scanner, the location accuracy of the 3D point cloud data is mainly (1) Position accuracy of the measurement vehicle by GPS (2) The attitude angle accuracy of the measurement vehicle estimated using an IMU, a GPS gyro, or the like is determined.

  (1) Regarding the position accuracy of the measurement vehicle by GPS, even if the GPS receiver mounted on the measurement vehicle receives the GPS signal and the Fix solution is obtained by the positioning calculation, the positioning originally possessed by GPS Degradation of position accuracy occurs due to error factors. In the field of static surveying, measurement is performed in a stationary state for a long time, and a time average solution is obtained to obtain a more accurate positioning result. However, unlike the case of static surveying, position measurement using a measuring vehicle that is a moving body cannot be performed in a stationary state or cannot run at a low speed. However, there is a problem that the method is not applicable.

  (2) Regarding the attitude angle accuracy of the measurement vehicle, even when the GPS gyro measurement is effective, that is, even when the attitude angle of the vehicle can be predicted by a plurality of GPS antennas mounted on the measurement vehicle, The attitude angle accuracy deteriorates due to the positioning error factor that is inherently present. In the case of stationary surveying, positioning accuracy can be improved by standing still for a long time and averaging the time, but in the case of mobile surveying, it is usually impossible to stop or run at low speed, so the accuracy is improved. On the other hand, there is a problem that the method used in the static survey cannot be applied.

  The present invention has been made to solve such problems, and even in the case of mobile body surveying by a measuring vehicle, three-dimensional point cloud measurement capable of generating three-dimensional point cloud data with high positional accuracy by post-processing. It aims to provide a method.

  A three-dimensional point cloud measurement method according to the present invention is a data group collected while a vehicle equipped with a laser scanner, a GPS receiver, and an attitude angle detector is traveling, and is a laser beam scanned by the laser scanner. Distance azimuth data consisting of the distance to the surrounding features of the vehicle and the azimuth to the features measured by the vehicle, position data of the vehicle acquired by the GPS receiver, and the attitude angle detector Using the data group in which the acquired attitude angle data of the vehicle is associated with the acquisition time when the distance azimuth data, the position data, and the attitude angle data are acquired, the laser light is emitted. A three-dimensional point cloud measurement method for measuring a coordinate position of a three-dimensional point cloud composed of irradiation points of the feature that has been obtained, wherein the vehicle travels on the same travel route a plurality of times and is acquired for each travel. Using data group, by weighting the data set based on the reliability of each group of data to calculate the coordinate position of the three-dimensional point cloud.

According to the three-dimensional point cloud measurement method according to the present invention, it is possible to acquire a highly accurate coordinate position for the three-dimensional point cloud by post-processing.

3 is a diagram for explaining a three-dimensional point cloud measurement method according to Embodiment 1. FIG. 6 is a diagram for explaining a three-dimensional point cloud measurement method according to Embodiment 2. FIG. 10 is a diagram for explaining a three-dimensional point cloud measurement method according to Embodiment 3. FIG. 10 is a diagram for explaining a three-dimensional point cloud measurement method according to Embodiment 4. FIG. FIG. 10 is a diagram for explaining a three-dimensional point cloud measurement method according to a fifth embodiment. FIG. 10 is a diagram for explaining a three-dimensional point cloud measurement method according to a sixth embodiment. FIG. 10 is a diagram for explaining a three-dimensional point cloud measurement method according to a seventh embodiment. It is a figure explaining the structure of the measurement vehicle which concerns on Embodiment 1-7. It is a figure explaining the structure of the self-position measuring apparatus which concerns on Embodiment 1-7.

  The three-dimensional point cloud measurement method according to the present invention uses the at least one method described in the first to seventh embodiments described below to determine the position of a three-dimensional point cloud (a set of three-dimensional coordinate points). Measure. Of the measurement methods described in the first to seventh embodiments, only one of the measurement methods may be executed, or a plurality of measurement methods may be executed in combination.

FIG. 8 is a diagram showing a schematic configuration of a measurement vehicle 100 that collects data of a three-dimensional point group in the first to seventh embodiments according to the present invention. The measurement vehicle 100 has the same function as the measurement vehicle described in Patent Document 2.
FIG. 9 is a diagram illustrating a configuration of a mobile body self-position measuring device (also simply referred to as a measuring device) 200 that measures the coordinate position of a three-dimensional point group with high accuracy by post-processing.
In the first to seventh embodiments, data of a three-dimensional point group is collected using a measurement vehicle equivalent to the measurement vehicle described in Patent Document 2, and the self-position measuring device 200 described in Patent Document 2 is provided. The feature position measurement unit 213 uses the three-dimensional point group of the feature (the feature located in the measurement target area) reflected in the camera image 281 based on the three-dimensional point group 292 projected on the camera image 281. The coordinates are calculated using the CPU. The details of the configuration of the measurement vehicle 100 and the mobile unit self-position measurement device 200 are described in Patent Document 2, and the outline thereof will be described here.

  The measurement vehicle 100 in FIG. 8 travels in the measurement target area and acquires various data necessary for generating, for example, a three-dimensional map of the measurement target area.

In the measurement vehicle 100, a measurement unit 110 is attached to a top plate. In addition, the measurement vehicle 100 includes a unit storage unit 190 (storage device).
The measurement unit 110 includes a front camera 111, a road surface camera 112, a GPS 113, an upper surface laser scanner 114, a lower surface laser scanner 115, an IMU 116, and a wiring relay box 117.

  The front camera 111 is attached horizontally and images the front of the measurement vehicle 100. The road surface camera 112 is attached obliquely downward and images the road surface.

  GPS113 is installed in three places. Each GPS 113 is a device that receives a positioning signal (indicating satellite orbit information and the like) transmitted from a GPS satellite and performs GPS positioning based on a reception result (GPS observation value).

The upper surface laser scanner 114 is mounted obliquely upward, and measures a distance direction for a feature (for example, a road sign) located at a location higher than the measurement unit 110.
The lower surface laser scanner 115 is mounted obliquely downward, and measures the distance direction for a feature (for example, a road surface) located at a place lower than the measurement unit 110.
The distance direction means the distance and direction from the upper surface laser scanner 114 or the lower surface laser scanner 115 to the feature.
In the measurement of the distance direction, the upper surface laser scanner 114 and the lower surface laser scanner 115 sequentially emit laser beams in the direction of 180 degrees from the left side to the front and from the front side to the right side while descending from side to side. Measure the time it takes for the object to reflect back and return. The upper surface laser scanner 114 and the lower surface laser scanner 115 calculate the distance from the feature reflecting the laser based on the measured time, and the calculated distance and the laser emission direction are set as the distance direction of the feature. .
The laser scanner is also referred to as LRF (Laser Range Finder).

  The IMU 116 (IMU: Inertial Navigation Device) measures angular velocities in three axial directions (x, y, z). For example, a gyro is used as the IMU 116.

  Each of the above devices outputs the acquired data to the wiring relay box 117 together with the acquisition time (measurement time).

In FIG. 9, an acquired data storage unit 280 is a storage device that stores each data acquired by the measurement vehicle 100 using a storage medium. The storage unit 280 stores data output to the wiring relay BOX 117.
The measurement data storage unit 290 is a storage device that stores the vehicle position and orientation 291, the three-dimensional point group 292, and the feature position 293 using a storage medium.
The feature position measurement unit 213 uses the three-dimensional coordinates (features located in the measurement target area) reflected in the camera image 281 based on the three-dimensional point group 292 projected on the camera image 281 ( The coordinate position is calculated using the CPU.

  In the following first to seventh embodiments, a method for measuring the coordinate position of a three-dimensional point group executed by the feature position measuring unit 213 will be described.

Embodiment 1 FIG.
The three-dimensional point cloud measurement method according to Embodiment 1 travels a road to be measured a plurality of times and collects various data for each travel. The measurement accuracy is improved by averaging the measurement results of the three-dimensional point group for each run. Alternatively, the measurement accuracy is improved by calculating a weighted coordinate position according to the reliability of the positioning information for each run.

FIG. 1 is a diagram for explaining a measurement method of the three-dimensional point cloud measurement method according to the first embodiment.
The measurement vehicle 100 travels the same traveling path (for example, between the position A and the position B) a plurality of times. The traveling direction may be the same direction (direction from position A to position B) or the opposite direction (direction from position B to position A).

In the laser point group (three-dimensional point group 292) collected by the laser scanners 114 and 115 by a plurality of runs, data of a three-dimensional point group corresponding to a fixed object whose position does not change is extracted.

Examples of fixed objects whose positions do not change include utility poles, traffic lights, buildings, and the like. On the other hand, a vehicle, a person, a bicycle, etc. are mentioned as an example of what changes a position. Here, data of a three-dimensional point group corresponding to a fixed object whose position does not change is extracted.
Next, the coordinate position of the three-dimensional point group corresponding to the fixed object whose position does not change is calculated for each run of the three-dimensional point group data acquired by a plurality of runs. The average processing is performed using the coordinate position of the fixed object calculated in each run, and the result is regarded as the true coordinate position of the three-dimensional point group corresponding to the fixed object.

In the example described in FIG. 1, a fixed object (for example, a power pole) whose position does not change is extracted from the three-dimensional point group acquired when the measurement vehicle is at position A in the first run, and the extracted fixed object The coordinate position of the three-dimensional point group corresponding to is obtained.
Also, in the first run, a fixed object (for example, a traffic light) whose position does not change is extracted from the three-dimensional point group acquired when the measurement vehicle is at position B, and the three-dimensional point corresponding to the extracted fixed object Find the coordinate position of the group.
Similarly in the second run, the same fixed object (electric pole) as the first time is extracted from the three-dimensional point cloud acquired when the measurement vehicle is at position A, and corresponds to the extracted fixed object. Find the coordinate position of a 3D point cloud. Further, the same fixed object (signal) as the first time is extracted from the three-dimensional point group acquired when the measurement vehicle is at position B, and the coordinate position of the three-dimensional point group corresponding to the extracted fixed object is extracted. Ask for.

Using the coordinate position data of the common fixed objects (electric poles, traffic lights) acquired during the first and second runs, and averaging these data, the result is the fixed object at position A. It is regarded as the true coordinate position of the (electric pole), and is regarded as the true coordinate position of the fixed object (signal) at the position B.
By taking the difference from the true coordinate position, the correction amount at position A and the correction amount at position B in the first run are obtained. Similarly, the correction amount at the position A and the correction amount at the position B in the second run are obtained.

Since the correction amount of two points (electric pole and traffic light) is calculated for the data of the coordinate position acquired in the first run in this way, the correction amount is proportionally distributed based on the correction amount of these two points. It is possible to correct the entire coordinate position data of the three-dimensional point group acquired in the second run.
Similarly, the entire coordinate position data of the three-dimensional point group acquired in the second run can be corrected.
In this way, it is possible to correct the entire coordinate position data of the three-dimensional point group by expanding and contracting the laser point group measured in each run so that fixed objects (electric poles, traffic lights) that do not change overlap.

The measurement vehicle 100 travels between position A and position B by performing the averaging process using the coordinate position data corrected by the first run and the coordinate position data obtained by the second run. The coordinate position of the acquired three-dimensional point group can be made into highly accurate data.

  FIG. 1 illustrates an example in which various data are collected by traveling twice on the same traveling route, but the same applies to the case of traveling 3 degrees or more (n degrees (n = 3, 4,...)). Thus, the coordinate position of the three-dimensional point group can be corrected by expanding and contracting the laser point group measured in each run so that fixed objects that do not change overlap.

When the reliability of the data acquired in the first run and the second run is different, each data may be weighted to calculate the final coordinate position of the three-dimensional point group. .
For example, when the reliability of the first traveling result is low and the reliability of the second traveling result is high, the final three-dimensional point group coordinate position is calculated by weighting the second traveling result. It may be.

Regarding the reliability of data, for example, even when the calculation result is fixed by a positioning calculation using a GPS signal, the reliability of the position measurement result is high because the number of GPS satellites used for the positioning calculation is large or the DOP is small. Sex changes. Therefore, the level of reliability may be obtained according to the DOP result, weighting may be performed with the reliability of position accuracy, and the average result may be handled as a true value.

  As described above, in the three-dimensional point cloud measurement method according to the first embodiment, the state of the satellite constantly changes while the measurement vehicle is traveling, so that correction is not performed based on one fixed object but at regular intervals. A fixed object whose position does not change is specified, and a position correction amount for each run is determined.

  Then, by averaging the errors that can originally occur in the position measurement by GPS, the GPS positioning error can be reduced and the overall positioning accuracy can be improved. That is, an error occurs for each measurement in a single GPS measurement, but an error is reduced by averaging a plurality of times of traveling results, and a more accurate measurement result can be obtained.

Embodiment 2. FIG.
In the first embodiment, the error is reduced by traveling on the same traveling path a plurality of times. However, in the second embodiment, a plurality of electronic reference points to be used for measurement are selected, and based on the data of each electronic reference point. Measure the position of the moving object.

FIG. 2 is a diagram for explaining a three-dimensional point cloud measurement method according to the second embodiment.
Specifically, when there are a plurality of electronic reference points in the vicinity, a post-processing operation of moving body position measurement is performed for each electronic reference point using the plurality of electronic reference points. Then, the position measurement results obtained for each electronic reference point are averaged, and the averaged result is regarded as a true value.
In the example of FIG. 2, there are three electronic reference points in the vicinity of the measurement area, and the positioning solution (positioning solution of reference point 1) acquired using the data of electronic reference point 1 and the data of electronic reference point 2 are used. The obtained positioning solution (the positioning solution of the reference point 2) and the positioning solution (the positioning solution of the reference point 3) acquired using the data of the electronic reference point 3 are respectively calculated. Then, the coordinate position of the final three-dimensional point group is acquired by averaging the positioning solution of the reference point 1, the positioning solution of the reference point 2, and the positioning solution of the reference point 3.

  The position measurement by GPS has an error due to the electronic reference point. However, in the three-dimensional point cloud measurement method according to the second embodiment, the electronic reference is calculated and averaged using a plurality of electronic reference points. Errors due to points can be reduced.

Embodiment 3 FIG.
In the second embodiment, the average of the positioning results obtained by using a plurality of electronic reference points in the vicinity is taken, but in the third embodiment, the path to be measured (position A to position) A new stationary private measurement station is installed in the vicinity of the route B), and the coordinate position of the three-dimensional point group by the measurement vehicle is corrected according to the temporal variation of the positioning result of the newly installed stationary measurement station.

FIGS. 3A and 3B are diagrams for explaining a three-dimensional point cloud measurement method according to the third embodiment.
FIG. 3A is a diagram showing the measurement result (measured value) of the three-dimensional point group by the measurement vehicle and the corrected measurement result (corrected value) for each time. FIG. 3B is a measurement result (measurement value) measured statically at a newly installed private private measurement station, and a measurement result of a fixed object such as a utility pole whose true position (true value) is known in advance ( It is the figure which showed (measured value) for every time.

  As shown in FIG. 3B, a static survey at a stationary point whose exact position (true value) is known in advance is performed in the same time zone as the measurement by the measurement vehicle, and each time (time T = T1, Correction values (difference between true value and measured value) 31 to 36 are calculated from the difference between the measurement result (measured value) and the true value at T2,.

  Next, the measurement results of the three-dimensional point group by the measurement vehicle are corrected by correcting the measurement results (measurement values) at the same time by the measurement vehicle by the correction values 31 to 36 calculated at each time.

As described above, in the three-dimensional point cloud measurement method according to the third embodiment, a stationary private measurement station is provided in the vicinity of the moving measurement vehicle. Then, assuming that the cause of the measurement error caused by the stationary private measurement station and the measurement vehicle generated at the same time is the same as the cause of the measurement error caused by the measurement vehicle, Was corrected.
According to the three-dimensional point cloud measurement method according to Embodiment 3, it is possible to reduce the measurement error of the three-dimensional point cloud using the measurement vehicle.

  In addition, since a stationary private measuring station installed in the vicinity receives the same satellite as the measuring vehicle and the effect of the ionosphere etc. is considered to be the same, it is possible to correct all measuring points. .

Embodiment 4 FIG.
In the third embodiment, a stationary private measurement station is newly installed in the vicinity of the measurement vehicle, and the coordinate position of the three-dimensional point group by the measurement vehicle is corrected based on the obtained positioning result. In the fourth embodiment, three-dimensional point cloud measurement is performed using only a common GPS satellite in a GPS satellite group in which the reception state changes with time.

  When a GPS signal is received and a positioning solution or azimuth is calculated, a larger number of GPS satellites are captured than the number of satellites required for position measurement when the measurement vehicle (moving body) travels. There is a case. For example, when the minimum number of satellites required for position measurement is 5, there may be a case where the measurement vehicle (moving body) captures 8 or more satellites.

FIG. 4 is a diagram for explaining a three-dimensional point cloud measurement method according to the fourth embodiment. Generally, when the measuring vehicle (moving body) travels, the number of GPS satellites captured by the GPS receiver mounted on the measuring vehicle changes. For example, as shown in FIG. 4A, the number of GPS satellites (number of reserved satellites) from which the GPS receiver has captured radio waves changes with time, such as 5, 6, 7, 6, and so on.
In general, if the GPS satellite used for the positioning calculation changes, the result of the positioning calculation changes, which becomes an error factor for three-dimensional point cloud measurement. FIG. 4B schematically shows a satellite that has received a radio wave and a positioning result by the received radio wave. As described above, the positioning result also varies depending on the satellite used for the positioning calculation.

Therefore, in the three-dimensional point cloud measurement method according to the fourth embodiment, the calculation is performed using only the common GPS satellite when performing the positioning calculation. That is, only GPS satellites that continuously capture radio waves from GPS satellites are used within the time when various data are acquired while the measurement vehicle is moving. In the example of the reception status of each satellite shown in FIG. 4A, radio waves from GPS satellites A, C, F, G, and H can be always received during measurement. In addition, a positioning result is obtained using only radio waves from GPS satellites A, C, F, G, and H.
Thereby, the fluctuation | variation (change) of the position by the increase / decrease in the number of GPS satellites used for a positioning calculation can be reduced.

Embodiment 5 FIG.
In the fourth embodiment, a three-dimensional point cloud is measured using a common GPS satellite. In the fifth embodiment, positioning calculation errors are reduced by mounting a two-frequency GPS on a GPS gyro.

  FIG. 5 is a diagram for explaining the positional relationship of the GPS antennas installed on the top plate of the measurement vehicle. In FIG. 5, it is assumed that the positional relationship between the three GPS antennas (GPS1 to GPS3) installed on the top plate of the measurement vehicle has been measured in advance and is known. Note that the GPS antennas 1 to 3 are all dual-frequency GPS antennas.

Next, the positioning results of GPS2 and GPS3 in FIG. 5 are moved to the position of GPS1 based on the known positional relationship. When the positioning results of GPS 2 and GPS 3 are moved to the position of GPS 1, the positioning error deviates from the position of GPS 1 and does not match.
An error can be obtained by repeating the operation of moving the positioning results of GPS2 and GPS3 to the position of GPS1 based on a known positional relationship, and averaging the amount of deviation from the position of GPS1.

  As described above, in the three-dimensional point cloud measurement method according to the fifth embodiment, a plurality of two-frequency receivers are mounted on the GPS gyro. Originally, the distance between GPS antennas is precisely measured with a GPS gyro, so that the positioning results of a plurality of two-frequency receivers are corrected to the origin and averaged to obtain an effect similar to time averaging. be able to.

Embodiment 6 FIG.
In the fifth embodiment, a plurality of two-frequency GPSs are mounted on a GPS gyro to reduce positioning calculation errors. In the sixth embodiment, each time a certain distance is traveled, the vehicle is stopped for a certain period of time to perform a static survey, and the position accuracy is improved by substituting as a GCS point.

  FIG. 6 is a diagram for explaining a three-dimensional point cloud measurement method according to the sixth embodiment. In the sixth embodiment, the vehicle is stopped for a fixed time at every fixed distance, and the three-dimensional point group is measured for a predetermined time in a stationary state. And the time average of a measurement result is taken (refer Fig.6 (a)). The averaged point is used as, for example, a GCP (Grand Control Point) point (see FIG. 6B).

  In general, even if the vehicle is a measurement vehicle, it is possible to improve the position accuracy by addition averaging by making the measurement vehicle stationary without moving and performing measurement in a stationary state. This is the same for positioning and posture. By performing the averaging for the stationary time, the position accuracy can be improved in the same manner as the stationary surveying by GPS. Thus, it is considered that the three-dimensional data of the laser point obtained from the average position and orientation with the vehicle stationary is higher in accuracy than the data at the time of traveling.

  As described above, according to the three-dimensional point cloud measurement method according to Embodiment 6, the positional accuracy of the three-dimensional point cloud is improved by using the time-averaged laser point as the GCP without preparing the GCP in advance. Can be made.

Embodiment 7 FIG.
In the seventh embodiment, a GPS gyro usually composed of three GPS antennas is composed of more antennas.

FIG. 7 shows an example of a GPS antenna arranged on the upper surface of the measurement vehicle, and is a diagram for explaining a three-dimensional point cloud measurement method according to the seventh embodiment.
In FIG. 7, four GPS antennas (GPS1 to GPS4) are mounted on the vehicle top plate. The configuration of the GPS gyro in this case includes (1) a combination of GPS1-GPS2-GPS3 (combination 1), (2) a combination of GPS1-GPS2-GPS4 (combination 2), and (3) a combination of GPS1-GPS3-GPS4. Four triangles of (combination 3) and (4) GPS2-GPS3-GPS4 combination (combination 4) can be configured. By calculating the attitude angle from each triangle and using the average value to calculate the attitude angle, the accuracy can be improved compared to the attitude angle result of one GPS gyro.

  In this way, the idea of the GPS gyro is to measure not only the position but also the posture by mounting two or more GPS antennas, but if three GPS antennas are mounted, the yaw angle (Yaw) and the roll angle (Roll) ), And three posture angles of pitch angle (Pitch) can be measured. Furthermore, if four GPS antennas are mounted, four triangles can be formed. If 5 are mounted, 10 triangles can be formed. By calculating the posture angle using these triangles and performing the posture angle prediction using the average or the like, an average effect similar to the time average can be obtained.

  In the first to seventh embodiments, the GPS satellite is used as the positioning satellite. However, the GPS satellite is an example, and the present invention has the same effect even if it is a satellite such as GLONASS or Galileo.

  1, 2, 3, 4 GPS antenna, 31-36 Each correction amount in the third embodiment, 100 measurement vehicle, 111, 112 camera, 113 GPS antenna, 114, 115 laser scanner, 116 IMU, 200 mobile body self-position measurement Device, 213 Feature position measurement unit.

Claims (7)

A data group collected while a vehicle equipped with a laser scanner, a GPS receiver, and an attitude angle detector is traveling, up to features around the vehicle measured by laser light scanned by the laser scanner Distance and azimuth data consisting of the distance to the feature, the position data of the vehicle acquired by the GPS receiver, the attitude angle data of the vehicle acquired by the attitude angle detector, A three-dimensional group consisting of irradiation points of the feature irradiated with the laser beam, using a data group associated with the acquisition time when each of the distance azimuth data, the position data, and the attitude angle data is acquired A three-dimensional point cloud measurement method for measuring the coordinate position of a point cloud,
Using a data group for each travel obtained by the vehicle traveling a plurality of times on the same travel path, the coordinate position of the three-dimensional point group is calculated by weighting the data group based on the reliability of each data group. A three-dimensional point cloud measurement method characterized by this. A data group collected while a vehicle equipped with a laser scanner, a GPS receiver, and an attitude angle detector is traveling, up to features around the vehicle measured by laser light scanned by the laser scanner Distance and azimuth data consisting of the distance to the feature, the position data of the vehicle acquired by the GPS receiver, the attitude angle data of the vehicle acquired by the attitude angle detector, A three-dimensional group consisting of irradiation points of the feature irradiated with the laser beam, using a data group associated with the acquisition time when each of the distance azimuth data, the position data, and the attitude angle data is acquired A three-dimensional point cloud measurement method for measuring the coordinate position of a point cloud,
The GPS receiver receives positioning signals from GPS satellites and receives correction information distributed from a plurality of reference stations installed on the ground at predetermined intervals,
A three-dimensional point group measurement method, wherein the coordinate position of the three-dimensional point group is calculated using the coordinate position of the three-dimensional point group measured based on correction information distributed from each reference station. A data group collected while a vehicle equipped with a laser scanner, a GPS receiver, and an attitude angle detector is traveling, up to features around the vehicle measured by laser light scanned by the laser scanner Distance and azimuth data consisting of the distance to the feature, the position data of the vehicle acquired by the GPS receiver, the attitude angle data of the vehicle acquired by the attitude angle detector, A three-dimensional group consisting of irradiation points of the feature irradiated with the laser beam, using a data group associated with the acquisition time when each of the distance azimuth data, the position data, and the attitude angle data is acquired A three-dimensional point cloud measurement method for measuring the coordinate position of a point cloud,
A three-dimensional point that corrects a coordinate position of the three-dimensional point group based on a change over time of a measurement result of the measurement station measured by a stationary measurement station provided in the vicinity of an area where the vehicle travels Group measurement method. A data group collected while a vehicle equipped with a laser scanner, a GPS receiver, and an attitude angle detector is traveling, up to features around the vehicle measured by laser light scanned by the laser scanner Distance and azimuth data consisting of the distance to the feature, the position data of the vehicle acquired by the GPS receiver, the attitude angle data of the vehicle acquired by the attitude angle detector, A three-dimensional group consisting of irradiation points of the feature irradiated with the laser beam, using a data group associated with the acquisition time when each of the distance azimuth data, the position data, and the attitude angle data is acquired A three-dimensional point cloud measurement method for measuring the coordinate position of a point cloud,
Measure the coordinate position of the three-dimensional point group using only the positioning signals from the same GPS satellite among the positioning signals received by the GPS receiver between the disclosure of the measurement of the three-dimensional point group and the end of the measurement. 3D point cloud measurement method characterized by A data group collected while a vehicle equipped with a laser scanner, a GPS receiver, and an attitude angle detector is traveling, up to features around the vehicle measured by laser light scanned by the laser scanner Distance and azimuth data consisting of the distance to the feature, the position data of the vehicle acquired by the GPS receiver, the attitude angle data of the vehicle acquired by the attitude angle detector, A three-dimensional group consisting of irradiation points of the feature irradiated with the laser beam, using a data group associated with the acquisition time when each of the distance azimuth data, the position data, and the attitude angle data is acquired A three-dimensional point cloud measurement method for measuring the coordinate position of a point cloud,
A three-dimensional point characterized by measuring a coordinate position of the three-dimensional point group using a position of the vehicle measured statically in a stopped state as a GCP (Grand Control Point) point. Group measurement method. A data group collected while a vehicle equipped with a laser scanner, a GPS receiver, and an attitude angle detector is traveling, up to features around the vehicle measured by laser light scanned by the laser scanner Distance and azimuth data consisting of the distance to the feature, the position data of the vehicle acquired by the GPS receiver, the attitude angle data of the vehicle acquired by the attitude angle detector, A three-dimensional group consisting of irradiation points of the feature irradiated with the laser beam, using a data group associated with the acquisition time when each of the distance azimuth data, the position data, and the attitude angle data is acquired A three-dimensional point cloud measurement method for measuring the coordinate position of a point cloud,
The attitude angle detector is a GPS gyro, and the coordinates of the three-dimensional point group are obtained by using the attitude angle data of the vehicle calculated by a combination of three GPS antennas extracted from four or more GPS antennas. A three-dimensional point cloud measurement method characterized by measuring a position. A three-dimensional point group measurement program for measuring a coordinate position of the three-dimensional point group by the three-dimensional point group measurement method according to any one of claims 1 to 6.

Images