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CN113280798B - Geometry Correction Method for Vehicle Scanning Point Cloud in Tunnel GNSS Denied Environment - Google Patents

Geometry Correction Method for Vehicle Scanning Point Cloud in Tunnel GNSS Denied Environment Download PDF

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CN113280798B
CN113280798B CN202110820156.4A CN202110820156A CN113280798B CN 113280798 B CN113280798 B CN 113280798B CN 202110820156 A CN202110820156 A CN 202110820156A CN 113280798 B CN113280798 B CN 113280798B
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point cloud
vehicle
target
cloud data
tunnel
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CN113280798A (en
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李升甫
葛旭明
廖知勇
汪镇东
贾洋
汪致恒
刘霜辰
许濒支
姚周祥
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Southwest Jiaotong University
Sichuan Department of Transportation Highway Planning Prospecting and Design Research Institute
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Sichuan Department of Transportation Highway Planning Prospecting and Design Research Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C15/00—Surveying instruments or accessories not provided for in groups G01C1/00 - G01C13/00
    • G01C15/002—Active optical surveying means
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations

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Abstract

The invention belongs to the field of laser scanning surveying and mapping, and provides a geometric correction method for vehicle-mounted scanning point clouds in a tunnel GNSS rejection environment, which can solve the problem of geometric correction of the point clouds in a tunnel region due to non-rigid deformation, so that the requirement of high-precision point cloud collection is met. Firstly, grouping and distributing targets along the extending direction of a tunnel; secondly, mapping and acquiring point cloud data through a vehicle-mounted scanning system, and acquiring calibration point coordinates of each target through actual measurement; thirdly, extracting three-dimensional coordinates of the same-name points of the corresponding targets from the point cloud data obtained by the vehicle-mounted scanning system; then, calculating conversion parameters between the actual measurement target coordinates and the point cloud target coordinates in groups, correcting the track positions of the vehicle-mounted scanning system corresponding to the targets in each group, and completing the track of the mobile measurement system in other track areas to be corrected by an interpolation method; and finally, re-resolving by using the corrected accurate track of the mobile measurement system to obtain high-precision long tunnel point cloud data.

Description

隧道GNSS拒止环境下车载扫描点云的几何纠正方法Geometry Correction Method for Vehicle Scanning Point Cloud in Tunnel GNSS Denied Environment

技术领域technical field

本发明属于激光扫描测绘领域，具体涉及一种隧道GNSS拒止环境下车载扫描点云的几何纠正方法。The invention belongs to the field of laser scanning surveying and mapping, and in particular relates to a geometric correction method for vehicle-mounted scanning point clouds in a tunnel GNSS rejection environment.

背景技术Background technique

随着车载移动三维激光测量系统的发展，该技术作为一种地理空间数据获取方式，已成为大场景三维点云数据采集的主要数据来源，具有安全、精确、高效的特点，在道路交通等相关领域的应用中发挥着至关重要的作用，在公路的规划管理、运营维护、数字建模及资产要素提取等方面具有巨大的潜力。With the development of vehicle-mounted mobile 3D laser measurement system, this technology, as a method of geospatial data acquisition, has become the main data source for 3D point cloud data collection in large scenes. It has the characteristics of safety, accuracy and efficiency. It plays a vital role in the application in the field of highway, and has great potential in highway planning management, operation and maintenance, digital modeling and asset element extraction.

但该技术的点云数据采集精度依赖于良好的连续GNSS信号，所述GNSS即GlobalNavigation Satellite System的简称，中文全称为全球导航卫星系统。因此，在长度大于3千米的长隧道的测绘中，由于GNSS信号的缺失，车载移动测量系统的位姿数据仅能通过惯性导航系统和里程计提供，从而导致序贯式的误差累积，造成所采集的隧道内部点云数据空间位置失准，无法满足高精度点云数据采集的需求。However, the point cloud data collection accuracy of this technology relies on good continuous GNSS signals, which is the abbreviation of Global Navigation Satellite System, and the full name in Chinese is Global Navigation Satellite System. Therefore, in the surveying and mapping of long tunnels longer than 3 kilometers, due to the lack of GNSS signals, the pose data of the vehicle-mounted mobile measurement system can only be provided by the inertial navigation system and odometer, resulting in sequential error accumulation, resulting in The spatial position of the collected point cloud data inside the tunnel is inaccurate, which cannot meet the needs of high-precision point cloud data collection.

为解决上述位置失准的问题，在现有技术中，通常在移动扫描点云作业中，通过在作业区域的GNSS缺失区域均匀布设密集的控制点，然后通过在具有误差的点云中提取对应的同名点，进行七参数转换来进行点云的纠正。In order to solve the problem of the above position inaccuracy, in the prior art, usually in the mobile scanning point cloud operation, dense control points are evenly arranged in the GNSS missing area of the operation area, and then the corresponding points are extracted from the point cloud with errors. The point cloud of the same name is corrected by seven-parameter transformation.

但该方法在长隧道GNSS拒止环境下，由于点云扫描测绘过程中，仅通过INS（全称Inertial Navigation System，即惯性导航系统）提供车载移动扫描系统的瞬时位置，通过其解算得到的点云为非刚体变形，并且隧道区域为条状，因此，并不能满足对点云进行几何纠正的要求，且作业难度大，数据量大，具体实施极为困难。However, in the long tunnel GNSS rejection environment, due to the point cloud scanning and mapping process, the instantaneous position of the vehicle-mounted mobile scanning system is only provided by the INS (full name Inertial Navigation System, that is, the inertial navigation system), and the point obtained through its calculation The cloud is non-rigid deformation, and the tunnel area is strip-shaped. Therefore, it cannot meet the requirements of geometric correction of the point cloud, and the operation is difficult, the amount of data is large, and the specific implementation is extremely difficult.

发明内容SUMMARY OF THE INVENTION

本发明所要解决的技术问题是提供一种隧道GNSS拒止环境下车载扫描点云的几何纠正方法，其能有效地解决：在隧道测绘中，尤其是长隧道测绘中，由于测绘过程GNSS信号缺失而导致的所采集点云数据的非刚体变形问题。The technical problem to be solved by the present invention is to provide a geometric correction method for vehicle-mounted scanning point cloud in a tunnel GNSS rejection environment, which can effectively solve: in tunnel surveying and mapping, especially in long tunnel surveying and mapping, due to the lack of GNSS signals during the surveying and mapping process The resulting non-rigid deformation problem of the collected point cloud data.

本发明解决上述技术问题采用的技术方案是：隧道GNSS拒止环境下车载扫描点云的几何纠正方法，包括以下步骤：The technical solution adopted by the present invention to solve the above-mentioned technical problems is: a method for geometric correction of vehicle-mounted scanning point clouds in a tunnel GNSS rejection environment, comprising the following steps:

S1、在隧道区域内，进行实地的标靶布设，标靶沿隧道延伸方向分组布设，每组包括4个以上的标靶，组间间隔设置；S1. In the tunnel area, carry out on-site target layout, and the targets are arranged in groups along the extension direction of the tunnel, each group includes more than 4 targets, and the groups are set at intervals;

S2、通过车载扫描系统测绘，获取包括各标靶点云数据在内的隧道点云数据；通过隧道测量控制网，实测各标靶的校准点坐标；S2. Obtain the tunnel point cloud data including the point cloud data of each target through surveying and mapping by the vehicle-mounted scanning system; measure the calibration point coordinates of each target through the tunnel measurement control network;

S3、对隧道点云数据进行处理，提取点云数据中对应的各标靶点云数据，并根据提取的各标靶点云数据，进一步获取各标靶点云数据中与实测标靶校准点对应的同名点坐标；S3. Process the tunnel point cloud data, extract the corresponding target point cloud data in the point cloud data, and further obtain the calibration points in the target point cloud data and the measured target according to the extracted target point cloud data. The corresponding coordinates of the point with the same name;

S4、通过实测的各组标靶的校准点坐标及与其对应的标靶点云数据中的同名点坐标，计算得到对应于每组标靶的转换参数，分别利用各组标靶对应的转换参数纠正与其布设位置相对应位置处的车载扫描系统的轨迹位置，获取纠正后的车载扫描系统的整体移动轨迹；S4. Calculate the conversion parameters corresponding to each group of targets through the measured calibration point coordinates of each group of targets and the coordinates of the points with the same name in the corresponding target point cloud data, and use the corresponding conversion parameters of each group of targets respectively. Correct the trajectory position of the vehicle-mounted scanning system at the position corresponding to its deployment position, and obtain the overall movement trajectory of the corrected vehicle-mounted scanning system;

S5、根据纠正后的车载扫描系统的整体移动轨迹，将隧道点云数据从车载扫描系统的激光扫描坐标系解算到世界坐标系，并重新生成隧道点云。S5. Calculate the tunnel point cloud data from the laser scanning coordinate system of the vehicle scanning system to the world coordinate system according to the corrected overall movement trajectory of the vehicle scanning system, and regenerate the tunnel point cloud.

作为进一步优化，步骤S1中，各组标靶间沿隧道延伸方向的间距为200~1000m，组内各标靶沿隧道延伸方向的布设长度小于10m，且组内各标靶布设于不同水平面；各标靶沿隧道延伸方向的边长d>0.08*f/v，其中，f为车载扫描系统的激光扫描频率，v为车载扫描系统的行驶速度。As a further optimization, in step S1, the distance between the targets in each group along the extension direction of the tunnel is 200-1000m, the arrangement length of each target in the group along the extension direction of the tunnel is less than 10m, and the targets in the group are arranged on different horizontal planes; The side length d>0.08*f/v of each target along the extension direction of the tunnel, where f is the laser scanning frequency of the on-board scanning system, and v is the traveling speed of the on-board scanning system.

作为进一步优化，步骤S1布设标靶所采用材料的反射率高于隧道场景的反射率，两者反射率的差异应满足基于强度值的点云数据提取；标靶的形状具备满足基于形状的点云标靶模板匹配所要求的形状特征。As a further optimization, the reflectivity of the material used to lay the target in step S1 is higher than that of the tunnel scene, and the difference between the two reflectivities should satisfy the point cloud data extraction based on the intensity value; The cloud target template matches the required shape features.

作为进一步优化，步骤S3中，对隧道点云数据进行处理，提取点云数据中对应的各标靶点云数据，并根据提取的各标靶点云数据，进一步获取各标靶点云数据中与实测标靶校准点对应的同名点坐标，具体包括：As a further optimization, in step S3, the tunnel point cloud data is processed, the corresponding target point cloud data in the point cloud data is extracted, and according to the extracted target point cloud data, the The coordinates of the point with the same name corresponding to the measured target calibration point, including:

S31、对点云数据进行强度采样：在车载扫描系统获取的点云数据中，随机选取部分标靶的点云数据进行强度值采样，确定标靶点云数据的强度值范围；S31. Perform intensity sampling on the point cloud data: in the point cloud data obtained by the vehicle-mounted scanning system, randomly select the point cloud data of some targets for intensity value sampling, and determine the intensity value range of the target point cloud data;

S32、对点云数据进行信息增强：通过S31确定的标靶点云数据的强度值范围，对点云数据进行强度值拉伸；S32, performing information enhancement on the point cloud data: stretching the intensity value of the point cloud data through the intensity value range of the target point cloud data determined in S31;

S33、标靶点云数据的粗提取；通过对S32增强后的点云数据进行直方图统计，根据强度信息剔除点云数据中异于所确定标靶点云数据强度值范围的其他点云数据，得到粗提取的标靶点云数据；S33. Rough extraction of target point cloud data; by performing histogram statistics on the point cloud data enhanced by S32, and eliminating other point cloud data in the point cloud data that differ from the intensity value range of the determined target point cloud data according to the intensity information , to obtain the roughly extracted target point cloud data;

S34、将粗提取获得的标靶点云数据与预制的标准点云标靶模板进行形状匹配，得到精提取的标靶点云数据；S34, performing shape matching between the target point cloud data obtained by the rough extraction and the prefabricated standard point cloud target template to obtain the refined target point cloud data;

S35、根据精提取的标靶点云数据，提取标靶点云数据中与实测标靶校准点对应的同名点坐标。S35 , according to the precisely extracted target point cloud data, extract the coordinates of the point of the same name corresponding to the measured target calibration point in the target point cloud data.

作为进一步优化，步骤S4具体包括：As a further optimization, step S4 specifically includes:

S41、通过实测的各组标靶的校准点坐标及与其对应的标靶点云数据中的同名点坐标，分别计算每组实测校准点坐标及与其对应的点云同名点坐标的转换参数；S41, through the measured calibration point coordinates of each group of targets and the same name point coordinates in the target point cloud data corresponding to them, respectively calculate the conversion parameters of each group of measured calibration point coordinates and its corresponding point cloud with the same name point coordinates;

S42、通过对应于每组标靶的转换参数，对与其布设位置相对应位置处的车载扫描系统的轨迹位置进行纠正；S42, correcting the trajectory position of the vehicle-mounted scanning system at the position corresponding to its deployment position through the conversion parameters corresponding to each group of targets;

S43、通过插值方法对相邻各组标靶间的车载扫描系统的轨迹位置进行补全，获取纠正后的车载扫描系统的整体移动轨迹。S43 , complementing the trajectory positions of the vehicle-mounted scanning systems between adjacent groups of targets through an interpolation method to obtain the corrected overall movement trajectory of the vehicle-mounted scanning system.

作为进一步优化，步骤S41中，所述计算每组实测校准点坐标及与其对应的点云同名点坐标的转换参数，具体包括：通过奇异值分解的方法计算每组实测校准点坐标及与其对应的点云同名点坐标的旋转矩阵R与平移矩阵T。As a further optimization, in step S41, the calculation of the coordinates of each group of measured calibration points and the conversion parameters of the coordinates of the point cloud with the same name corresponding to them specifically includes: calculating the coordinates of each group of measured calibration points and their corresponding coordinates by singular value decomposition. The rotation matrix R and the translation matrix T of the coordinates of the point cloud with the same name.

作为进一步优化，步骤S42中，所述通过对应于每组标靶的转换参数，对与其布设位置相对应位置处的车载扫描系统的轨迹位置进行纠正，具体包括：As a further optimization, in step S42, the trajectory position of the vehicle-mounted scanning system at the position corresponding to its deployment position is corrected through the conversion parameters corresponding to each group of targets, specifically including:

S421、根据每组标靶点云数据的激光扫描时刻，在待纠正的车载扫描系统的POS数据中，确定扫描该组标靶时的车载扫描系统的轨迹位置

Figure 245458DEST_PATH_IMAGE001
；S421. According to the laser scanning time of each group of target point cloud data, in the POS data of the vehicle-mounted scanning system to be corrected, determine the trajectory position of the vehicle-mounted scanning system when scanning the group of targets
Figure 245458DEST_PATH_IMAGE001
;

S422、通过对应组标靶的转换参数，对对应的待纠正车载扫描系统轨迹位置

Figure 755199DEST_PATH_IMAGE001
，利用旋转矩阵R与平移矩阵T进行纠正，获得纠正后的车载扫描系统轨迹位置
Figure 264459DEST_PATH_IMAGE002
：S422. Through the conversion parameters of the corresponding group of targets, the corresponding trajectory position of the vehicle-mounted scanning system to be corrected
Figure 755199DEST_PATH_IMAGE001
, use the rotation matrix R and the translation matrix T to correct, and obtain the corrected trajectory position of the vehicle scanning system
Figure 264459DEST_PATH_IMAGE002
:

Figure 715775DEST_PATH_IMAGE003
。
Figure 715775DEST_PATH_IMAGE003
.

作为进一步优化，步骤S42中，针对各组标靶，根据组内各标靶对应的扫描时刻，在待纠正的车载扫描系统的POS数据中，查找与该标靶扫描时刻时间相邻的轨迹位置采样时刻：As a further optimization, in step S42, for each group of targets, according to the scanning time corresponding to each target in the group, in the POS data of the vehicle-mounted scanning system to be corrected, search for the trajectory position adjacent to the target scanning time and time Sampling time:

若存在标靶扫描时刻与对应轨迹位置采样时刻相同，则以该轨迹位置采样时刻所采样的轨迹位置，作为该组标靶对应的待纠正的车载扫描系统的轨迹位置

Figure 621546DEST_PATH_IMAGE001
，若组内多个标靶均存在标靶扫描时刻与对应轨迹位置采样时刻相同，则随机选取一个；If there is a target scanning time that is the same as the sampling time of the corresponding trajectory position, the trajectory position sampled at the trajectory position sampling time is used as the trajectory position of the vehicle-mounted scanning system to be corrected corresponding to the group of targets
Figure 621546DEST_PATH_IMAGE001
, if multiple targets in the group have the same target scanning time as the sampling time of the corresponding trajectory position, select one randomly;

若不存在标靶扫描时刻与对应轨迹位置采样时刻相同，则确定该标靶扫描时刻时间前后两侧的轨迹位置采样时刻，以满足于If there is no target scanning time that is the same as the sampling time of the corresponding trajectory position, then determine the trajectory position sampling time on both sides before and after the target scanning time, so as to satisfy the

Figure 266021DEST_PATH_IMAGE004
Figure 266021DEST_PATH_IMAGE004

的轨迹位置扫描时刻作为该标靶扫描时刻所对应的轨迹位置采样时刻，其中，

Figure 878005DEST_PATH_IMAGE005
为标靶扫描时刻，
Figure 838789DEST_PATH_IMAGE006
为轨迹位置采样时刻，s为轨迹位置数据的采样间隔时间；根据组内各标靶扫描时刻与其对应的轨迹位置采样时刻的差值，以差值最小的轨迹位置采样时刻所采样的轨迹位置，作为该组标靶对应的待纠正的车载扫描系统的轨迹位置
Figure 407305DEST_PATH_IMAGE001
。The scanning time of the trajectory position is taken as the sampling time of the trajectory position corresponding to the scanning time of the target, wherein,
Figure 878005DEST_PATH_IMAGE005
Scan time for the target,
Figure 838789DEST_PATH_IMAGE006
is the sampling time of the trajectory position, s is the sampling interval time of the trajectory position data; according to the difference between the scanning time of each target in the group and the sampling time of the corresponding trajectory position, the trajectory position sampled at the sampling time of the trajectory position with the smallest difference, The trajectory position of the vehicle-mounted scanning system to be corrected corresponding to the group of targets
Figure 407305DEST_PATH_IMAGE001
.

作为进一步优化，步骤S43中，通过线性内插方法对相邻各组标靶间的车载扫描系统的轨迹位置进行补全，具体包括：As a further optimization, in step S43, the trajectory position of the vehicle-mounted scanning system between the adjacent groups of targets is complemented by a linear interpolation method, which specifically includes:

S431、根据相邻两组标靶所对应的轨迹位置的采样时刻

Figure 381384DEST_PATH_IMAGE007
，以及车载扫描系统的轨迹位置采样间隔时间s，计算相邻车载扫描系统轨迹位置间的待纠正位置点个数n：S431, according to the sampling time of the trajectory positions corresponding to the adjacent two groups of targets
Figure 381384DEST_PATH_IMAGE007
, and the sampling interval s of the trajectory position of the on-board scanning system, and calculate the number n of the position points to be corrected between the trajectory positions of the adjacent on-board scanning system:

Figure 728052DEST_PATH_IMAGE008
Figure 728052DEST_PATH_IMAGE008

S432、通过纠正后的相邻车载扫描系统的轨迹位置的三维坐标

Figure 107955DEST_PATH_IMAGE009
，利用线性内插方法计算两点间n个待纠正点，获得纠正后的整体车载系统轨迹：S432, passing the corrected three-dimensional coordinates of the trajectory position of the adjacent vehicle-mounted scanning system
Figure 107955DEST_PATH_IMAGE009
, use the linear interpolation method to calculate n points to be corrected between two points, and obtain the corrected overall vehicle system trajectory:

Figure 73637DEST_PATH_IMAGE010
Figure 73637DEST_PATH_IMAGE010

Figure 989641DEST_PATH_IMAGE011
Figure 989641DEST_PATH_IMAGE011

Figure 100685DEST_PATH_IMAGE012
为第i个区间中的第j个待纠正点纠正前的位置坐标，
Figure 938191DEST_PATH_IMAGE013
为第i个区间中的第j个待纠正点纠正后的位置坐标，
Figure 661559DEST_PATH_IMAGE014
分别为对应采样时刻
Figure 697648DEST_PATH_IMAGE007
的纠正前的车载扫描系统轨迹位置坐标，
Figure 245173DEST_PATH_IMAGE009
分别为对应采样时刻
Figure 304395DEST_PATH_IMAGE007
的纠正后的车载扫描系统轨迹位置坐标。
Figure 100685DEST_PATH_IMAGE012
is the position coordinate of the jth point to be corrected in the ith interval before correction,
Figure 938191DEST_PATH_IMAGE013
is the corrected position coordinate of the jth point to be corrected in the ith interval,
Figure 661559DEST_PATH_IMAGE014
are the corresponding sampling times, respectively
Figure 697648DEST_PATH_IMAGE007
The coordinates of the vehicle scanning system trajectory position before correction,
Figure 245173DEST_PATH_IMAGE009
are the corresponding sampling times, respectively
Figure 304395DEST_PATH_IMAGE007
The corrected vehicle-mounted scanning system trajectory position coordinates.

作为进一步优化，步骤S5具体包括：As a further optimization, step S5 specifically includes:

S51、将点云数据从激光扫描坐标

Figure 365542DEST_PATH_IMAGE015
转换到车载系统坐标
Figure 990559DEST_PATH_IMAGE016
：S51. Convert the point cloud data from the laser scanning coordinates
Figure 365542DEST_PATH_IMAGE015
Convert to vehicle system coordinates
Figure 990559DEST_PATH_IMAGE016
:

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Figure 708985DEST_PATH_IMAGE017

其中，

Figure 255504DEST_PATH_IMAGE018
表示激光扫描仪在车载系统坐标系中的姿态，
Figure 163417DEST_PATH_IMAGE019
表示激光扫描仪在车载系统坐标系中的位置，均为激光扫描仪的标定参数；in,
Figure 255504DEST_PATH_IMAGE018
represents the attitude of the laser scanner in the vehicle system coordinate system,
Figure 163417DEST_PATH_IMAGE019
Indicates the position of the laser scanner in the vehicle system coordinate system, which are the calibration parameters of the laser scanner;

S52、将点云数据从车载系统坐标

Figure 862514DEST_PATH_IMAGE016
转换到世界坐标
Figure 502574DEST_PATH_IMAGE020
，重新生成隧道点云数据：S52. Convert the point cloud data from the coordinates of the vehicle system
Figure 862514DEST_PATH_IMAGE016
Convert to world coordinates
Figure 502574DEST_PATH_IMAGE020
, regenerate the tunnel point cloud data:

Figure 582394DEST_PATH_IMAGE021
其中，
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为车载坐标系到世界坐标系统的旋转矩阵，由车载系统原始POS数据提供，
Figure 516776DEST_PATH_IMAGE023
为车载坐标系到世界坐标系的平移参数，由纠正后的车载系统POS数据提供；
Figure 582394DEST_PATH_IMAGE021
in,
Figure 231681DEST_PATH_IMAGE022
It is the rotation matrix from the vehicle coordinate system to the world coordinate system, which is provided by the original POS data of the vehicle system.
Figure 516776DEST_PATH_IMAGE023
It is the translation parameter from the vehicle coordinate system to the world coordinate system, which is provided by the corrected vehicle system POS data;

在轨迹位置数据两帧之间采用均匀采样的方法得到每个原始点云对应的轨迹姿态和位置。The trajectory pose and position corresponding to each original point cloud are obtained by uniform sampling between the two frames of trajectory position data.

本发明的有益效果是：通过少量标靶组对车载移动测量系统的轨迹进行校正，然后根据校正后的轨迹重新生成高精度点云，有效地解决了GNSS长时间缺失的隧道区域点云非刚体变形的问题，为道路的数字化应用提供了可靠且精确的数据支撑；本发明中的点云纠正方法对不同隧道都保持良好的鲁棒性，尤其是针对长隧道，且算法流程简单，并具有较高的纠正精度和效率。The beneficial effect of the invention is that: the trajectory of the vehicle-mounted mobile measurement system is corrected by a small number of target groups, and then the high-precision point cloud is regenerated according to the corrected trajectory, which effectively solves the non-rigid body of the point cloud in the tunnel area that GNSS has been missing for a long time. The problem of deformation provides reliable and accurate data support for the digital application of roads; the point cloud correction method in the present invention maintains good robustness for different tunnels, especially for long tunnels, and the algorithm flow is simple and has the advantages of Higher correction accuracy and efficiency.

附图说明Description of drawings

图1是本发明实施例中的扫描点云几何纠正方法整体流程图。FIG. 1 is an overall flow chart of a method for geometric correction of a scanned point cloud in an embodiment of the present invention.

具体实施方式Detailed ways

本发明旨在提出一种隧道GNSS拒止环境下车载扫描点云的几何纠正方法，提高隧道，尤其是长隧道区域非刚体变形的点云纠正精度，从而满足高精度点云采集的需求。The present invention aims to propose a geometric correction method for vehicle scanning point cloud in tunnel GNSS rejection environment, improve the point cloud correction accuracy of non-rigid deformation in tunnels, especially in long tunnel areas, so as to meet the needs of high-precision point cloud collection.

由于车载扫描系统的激光扫描系统坐标系到世界坐标系的计算简式如下：Because the calculation formula of the laser scanning system coordinate system of the vehicle scanning system to the world coordinate system is as follows:

Figure 593316DEST_PATH_IMAGE024
Figure 593316DEST_PATH_IMAGE024

式中，

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为点云的世界坐标，
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为激光扫描系统采集的原始坐标，
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、
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为激光扫描坐标系到载体坐标系的标定转换参数，
Figure 729899DEST_PATH_IMAGE022
、
Figure 517727DEST_PATH_IMAGE023
为载体坐标系到世界坐标系的标定转换参数。In the formula,
Figure 911165DEST_PATH_IMAGE020
is the world coordinate of the point cloud,
Figure 347831DEST_PATH_IMAGE015
The original coordinates collected for the laser scanning system,
Figure 5209DEST_PATH_IMAGE018
,
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is the calibration conversion parameter from the laser scanning coordinate system to the carrier coordinate system,
Figure 729899DEST_PATH_IMAGE022
,
Figure 517727DEST_PATH_IMAGE023
It is the calibration conversion parameter from the carrier coordinate system to the world coordinate system.

在GNSS拒止环境下，

Figure 295190DEST_PATH_IMAGE022
为惯性导航系统提供的姿态信息，不存在误差累计；而
Figure 291414DEST_PATH_IMAGE023
仅通过惯性导航系统随时间的积分计算得到，存在序贯式的累计误差。因此，
Figure 255959DEST_PATH_IMAGE025
项不受GNSS影响，可知载体的轨迹与扫描结果点云之间为固联关系，通过计算实测标靶坐标与点云中的标靶坐标之间的转换参数即可对系统载体的轨迹进行纠正。In a GNSS-denied environment,
Figure 295190DEST_PATH_IMAGE022
The attitude information provided for the inertial navigation system has no error accumulation; and
Figure 291414DEST_PATH_IMAGE023
It is only calculated by the integration of the inertial navigation system over time, and there is a sequential cumulative error. therefore,
Figure 255959DEST_PATH_IMAGE025
The item is not affected by GNSS, and it can be seen that there is a solid relationship between the trajectory of the carrier and the point cloud of the scanning result. The trajectory of the system carrier can be corrected by calculating the conversion parameters between the measured target coordinates and the target coordinates in the point cloud. .

因此，在本发明中，首先，针对隧道环境制作标靶，并在长隧道区域往返两侧进行分组布设，标靶的设计应满足于标靶点云数据提取的需要，具体的与其提取算法有关，在本发明的实施例中通过强度值和形状提取，因此，标靶应具备与隧道场景的反射率的较大差异，并具备一定的形状特征；其次，实测布设标靶校准点的三维坐标，并从车载扫描系统获得的点云数据中提取对应标靶校准点的同名点三维坐标；然后分组计算实测标靶校准点坐标和点云标靶同名点坐标间的转换参数，对各组标靶对应位置处的车载扫描系统轨迹位置进行纠正，并在其他待纠正的轨迹区域通过插值方法获得纠正后的移动测量系统轨迹；最后利用纠正后的移动测量系统的准确轨迹重新解算得到高精度的隧道点云数据，整体流程如图1所示。Therefore, in the present invention, first of all, targets are made for the tunnel environment, and grouped on both sides of the long tunnel area. The design of the targets should meet the needs of target point cloud data extraction, specifically related to its extraction algorithm. , in the embodiment of the present invention, the intensity value and the shape are extracted. Therefore, the target should have a large difference in reflectivity from the tunnel scene, and have certain shape characteristics; secondly, the three-dimensional coordinates of the calibration point of the target layout are measured. , and extract the three-dimensional coordinates of the same-named point corresponding to the target calibration point from the point cloud data obtained by the vehicle-mounted scanning system; The trajectory position of the vehicle-mounted scanning system at the corresponding position of the target is corrected, and the corrected trajectory of the mobile measurement system is obtained by interpolation in other trajectory areas to be corrected; The overall process is shown in Figure 1.

实施例：Example:

本实施例提出的隧道GNSS拒止环境下车载扫描点云的几何纠正方法，包括以下步骤：The geometric correction method of the vehicle scanning point cloud in the tunnel GNSS rejection environment proposed in this embodiment includes the following steps:

S1、在隧道区域内，进行实地的标靶布设，标靶沿隧道延伸方向分组布设，每组包括4个以上的标靶，组间间隔设置；S1. In the tunnel area, carry out on-site target layout, and the targets are arranged in groups along the extension direction of the tunnel, each group includes more than 4 targets, and the groups are set at intervals;

S2、通过车载扫描系统测绘，获取包括各标靶点云数据在内的隧道点云数据；通过隧道测量控制网，实测各标靶的校准点坐标；S2. Obtain the tunnel point cloud data including the point cloud data of each target through surveying and mapping by the vehicle-mounted scanning system; measure the calibration point coordinates of each target through the tunnel measurement control network;

S3、对隧道点云数据进行处理，提取点云数据中对应的各标靶点云数据，并根据提取的各标靶点云数据，进一步获取各标靶点云数据中与实测标靶校准点对应的同名点坐标；S3. Process the tunnel point cloud data, extract the corresponding target point cloud data in the point cloud data, and further obtain the calibration points in the target point cloud data and the measured target according to the extracted target point cloud data. The corresponding coordinates of the point with the same name;

S4、通过实测的各组标靶的校准点坐标及与其对应的标靶点云数据中的同名点坐标，计算得到对应于每组标靶的转换参数，分别利用各组标靶对应的转换参数纠正与其布设位置相对应位置处的车载扫描系统的轨迹位置，获取纠正后的车载扫描系统的整体移动轨迹；S4. Calculate the conversion parameters corresponding to each group of targets through the measured calibration point coordinates of each group of targets and the coordinates of the points with the same name in the corresponding target point cloud data, and use the corresponding conversion parameters of each group of targets respectively. Correct the trajectory position of the vehicle-mounted scanning system at the position corresponding to its deployment position, and obtain the overall movement trajectory of the corrected vehicle-mounted scanning system;

S5、根据纠正后的车载扫描系统的整体移动轨迹，将激光点云数据从车载扫描系统的激光扫描坐标系解算到世界坐标系，并重新生成隧道点云。S5. According to the corrected overall movement trajectory of the vehicle-mounted scanning system, the laser point cloud data is calculated from the laser scanning coordinate system of the vehicle-mounted scanning system to the world coordinate system, and the tunnel point cloud is regenerated.

步骤S1，布设标靶，包括：Step S1, laying the target, including:

步骤S11，标靶制作：Step S11, target production:

为了便于实地量测以及从点云中提取标靶，布设标靶所采用材料的反射率高于隧道场景的反射率，两者反射率的差异应满足基于强度值的点云数据提取；标靶的形状具备满足基于形状的点云标靶模板匹配所要求的形状特征。In order to facilitate on-the-spot measurement and extraction of the target from the point cloud, the reflectivity of the material used to lay the target is higher than that of the tunnel scene, and the difference between the two reflectivity should satisfy the point cloud data extraction based on the intensity value; The shape has the shape features required for shape-based point cloud target template matching.

具体的，在本实施例中，标靶采用白色A4纸拼接构成背景板，并在背景板上通过粘贴十字交叉的铝箔形成反射率差异足够的标靶，以十字交叉点构成校准点方便测量。除本实施例的形状外，也可以采用圆形带中心点的标靶、顶点相交的两三角形构成的标靶等，其设计主要依据于测量及点云提取的方便；其材质的选取主要依据于反射率差异，可根据经验进行选择，本实施例选择A4纸及铝箔主要是方便获得。Specifically, in this embodiment, the target is spliced with white A4 paper to form a background plate, and crossed aluminum foil is pasted on the background plate to form a target with sufficient reflectivity difference, and the cross points constitute calibration points for convenient measurement. In addition to the shape of this embodiment, a circular target with a center point, a target composed of two triangles whose vertices intersect, etc. can also be used. The design is mainly based on the convenience of measurement and point cloud extraction; the material selection is mainly based on The difference in reflectivity can be selected according to experience. In this embodiment, A4 paper and aluminum foil are selected mainly for convenience.

步骤S12，标靶实地布设：Step S12, the target is laid out on the spot:

其一，由于计算转换参数至少需要4对以上的三维坐标，因此，进行实地的标靶布设时，标靶沿隧道延伸方向分组布设，每组包括4个以上的标靶，组间间隔设置。First, since the calculation of conversion parameters requires at least 4 pairs of three-dimensional coordinates, when deploying targets in the field, the targets are arranged in groups along the tunnel extension direction, and each group includes more than 4 targets, and the groups are set at intervals.

其二，为保证隧道点云纠正的均匀分布，标靶沿隧道延伸方向分组布设，组间间距根据需求精度指标决定，精度需求越高，则间隔越短，根据实验，每200米间距下各方向均方根误差不超过15厘米，足以满足高精度要求，因此，推荐的，各组标靶间沿隧道延伸方向的间距为200~1000m。所述各组标靶间沿隧道延伸方向的间距，即相邻两组标靶的相邻标靶沿隧道延伸方向，两者校准点之间的间距。Second, in order to ensure the uniform distribution of tunnel point cloud correction, the targets are arranged in groups along the extension direction of the tunnel, and the spacing between groups is determined according to the required accuracy index. The higher the accuracy requirement, the shorter the interval. The root mean square error of the direction does not exceed 15 cm, which is enough to meet the high precision requirements. Therefore, it is recommended that the distance between each group of targets along the extension direction of the tunnel be 200~1000m. The distance between the targets of each group along the extension direction of the tunnel, that is, the distance between adjacent targets of the adjacent two groups of targets along the extension direction of the tunnel, and the distance between the two calibration points.

其三，为保证转换参数的准确性，标靶应避免因间隔较远导致的非刚体变形，也即组内标靶的首次扫描时刻和末次扫描时间的时间差应足够小，比如在1秒以内，具体距离的设置与组内标靶数量、车载扫描系统的激光扫描频率和行驶速度有关。根据实验，在常规车载扫描系统下，推荐的，组内各标靶沿隧道延伸方向的布设长度小于10m。Third, in order to ensure the accuracy of the conversion parameters, the target should avoid non-rigid deformation caused by the long interval, that is, the time difference between the first scan time and the last scan time of the target in the group should be small enough, for example, within 1 second. , the setting of the specific distance is related to the number of targets in the group, the laser scanning frequency of the vehicle scanning system and the driving speed. According to the experiment, under the conventional vehicle-mounted scanning system, it is recommended that the length of each target in the group along the extension direction of the tunnel be less than 10m.

其四，为保证计算结果的鲁棒性，组内各标靶布设于不同水平面。Fourth, in order to ensure the robustness of the calculation results, the targets in the group are arranged at different levels.

其五，标靶的大小规格应根据车载扫描系统实际允许的行驶速度决定，以保证采集后落在标靶上的点云足够密集，因此，最优的，各标靶沿隧道延伸方向的边长d>0.08*f/v，其中，f为车载扫描系统的激光扫描频率，v为车载扫描系统的行驶速度。Fifth, the size and specification of the target should be determined according to the actual allowable driving speed of the vehicle-mounted scanning system to ensure that the point cloud falling on the target after collection is dense enough. Therefore, optimally, the edge of each target along the tunnel extension direction. Length d>0.08*f/v, where f is the laser scanning frequency of the vehicle-mounted scanning system, and v is the driving speed of the vehicle-mounted scanning system.

具体的，在本实施例中，每组包括4个标靶，当然，为了避免无效测量的干扰，最好的，可以设置为每组6个标靶。沿隧道延伸方向，各组标靶间的间距为200m，各组标靶组内布设的长度为1m，且布设于不同一水平面，沿隧道延伸方向由两张A4纸拼接构成。Specifically, in this embodiment, each group includes 4 targets. Of course, in order to avoid the interference of invalid measurement, it is best to set 6 targets in each group. Along the extension direction of the tunnel, the distance between each target group is 200m, the length of each target group is 1m, and they are arranged on different horizontal planes. Along the extension direction of the tunnel, they are composed of two pieces of A4 paper spliced together.

步骤S2，测量，包括：Step S2, measuring, including:

步骤S21，通过车载扫描系统测绘，获取包括各标靶点云数据在内的隧道点云数据。In step S21 , the tunnel point cloud data including the point cloud data of each target is acquired through surveying and mapping by the vehicle-mounted scanning system.

步骤S22，通过隧道测量控制网，实测各标靶的校准点坐标：Step S22, through the tunnel measurement control network, measure the calibration point coordinates of each target:

步骤S221，测量隧道内地面控制点的三维坐标：根据每组标靶在隧道内的布设位置，在隧道内选取地面控制点，并通过隧道测量控制网测量地面控制点的三维坐标。Step S221, measure the three-dimensional coordinates of the ground control points in the tunnel: select the ground control points in the tunnel according to the layout positions of each group of targets in the tunnel, and measure the three-dimensional coordinates of the ground control points through the tunnel measurement control network.

步骤S222，测量隧道两侧布设标靶校准点的三维坐标：在地面控制点的基础上，通过碎步测量获取在隧道两侧墙壁实际布设标靶的校准点的三维坐标。Step S222, measure the three-dimensional coordinates of the calibration points of the targets laid on both sides of the tunnel: on the basis of the ground control points, obtain the three-dimensional coordinates of the calibration points of the actual targets laid on the walls on both sides of the tunnel through step-by-step measurement.

步骤S3，坐标提取，包括：Step S3, coordinate extraction, including:

S31、对点云数据进行强度采样：在车载扫描系统获取的点云数据中，随机选取部分标靶的点云数据进行强度值采样，确定标靶点云数据的强度值范围。S31. Perform intensity sampling on the point cloud data: in the point cloud data obtained by the vehicle-mounted scanning system, randomly select the point cloud data of some targets for intensity value sampling, and determine the intensity value range of the target point cloud data.

S32、对点云数据进行信息增强：通过S31确定的标靶点云数据的强度值范围，对点云数据进行强度值拉伸。S32 , performing information enhancement on the point cloud data: according to the intensity value range of the target point cloud data determined in S31 , the intensity value of the point cloud data is stretched.

S33、标靶点云数据的粗提取；通过对S32增强后的点云数据进行直方图统计，根据强度信息剔除点云数据中异于所确定标靶点云数据强度值范围的其他点云数据，得到粗提取的标靶点云数据。S33. Rough extraction of target point cloud data; by performing histogram statistics on the point cloud data enhanced by S32, and eliminating other point cloud data in the point cloud data that differ from the intensity value range of the determined target point cloud data according to the intensity information , to obtain the roughly extracted target point cloud data.

S34、将粗提取获得的标靶点云数据与预制的标准点云标靶模板进行形状匹配，得到精提取的标靶点云数据。S34 , performing shape matching between the target point cloud data obtained by rough extraction and a prefabricated standard point cloud target template to obtain refined target point cloud data.

具体而言，根据布设的标靶位置分布的特性，按照距离阈值

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对粗提取标靶点云进行分割，得到粗提取标靶点云集
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，然后将标准标靶点云模板
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与点云集内每一组点云进行模板匹配：首先计算标靶点云的法向量主方向，然后将三维点云映射到二维平面上，得到标靶点云和标准模板的二维图像，最后通过二维图像模板匹配得到最终的精提取标靶结果。Specifically, according to the characteristics of the distribution of target positions, according to the distance threshold
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Segment the coarsely extracted target point cloud to obtain the coarsely extracted target point cloud set
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, and then the standard target point cloud template
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Template matching with each group of point clouds in the point cloud set: first calculate the main direction of the normal vector of the target point cloud, and then map the three-dimensional point cloud to the two-dimensional plane to obtain the two-dimensional image of the target point cloud and the standard template, Finally, the final refined target result is obtained by two-dimensional image template matching.

S35、根据精提取的标靶点云数据，提取标靶点云数据中与实测标靶校准点对应的同名点坐标。S35 , according to the precisely extracted target point cloud data, extract the coordinates of the point of the same name corresponding to the measured target calibration point in the target point cloud data.

同名点坐标可以采用计算机自动提取，但由于载体行驶非匀速行驶及遮挡等影响，采集得到的标靶点云不会完全保持原有形状，自动提取的坐标会导致转换精度存在一定程度的降低，因此，为保证精度，在本实施例中，对匹配后提取得到的点云标靶手动提取同名点的三维坐标并进行编号和分组，然后找到对应实测的标靶校准点坐标。The coordinates of the point with the same name can be automatically extracted by computer, but due to the influence of non-uniform driving and occlusion of the carrier, the collected target point cloud will not completely maintain the original shape, and the automatically extracted coordinates will lead to a certain degree of reduction in the conversion accuracy. Therefore, in order to ensure the accuracy, in this embodiment, the three-dimensional coordinates of the points with the same name are manually extracted from the point cloud target extracted after matching, numbered and grouped, and then the corresponding measured target calibration point coordinates are found.

步骤S4，轨迹纠正，包括：Step S4, trajectory correction, including:

S41、通过实测的各组标靶的校准点坐标及与其对应的标靶点云数据中的同名点坐标，分别计算每组实测校准点坐标及与其对应的点云同名点坐标的转换参数：S41, through the measured calibration point coordinates of each group of targets and the coordinates of the same name point in the corresponding target point cloud data, respectively calculate the conversion parameters of each group of measured calibration point coordinates and the coordinates of the corresponding point cloud with the same name point:

令点云标靶同名点三维坐标为

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，相应实测标靶校准点坐标为
Figure 650162DEST_PATH_IMAGE030
，计算每组点云标靶与实测标靶坐标的旋转矩阵R与平移矩阵T，即求解令下式最小时的最优解R，T： Let the three-dimensional coordinates of the point cloud target with the same name be
Figure 582849DEST_PATH_IMAGE029
, the corresponding measured target calibration point coordinates are
Figure 650162DEST_PATH_IMAGE030
, calculate the rotation matrix R and translation matrix T of each group of point cloud targets and the measured target coordinates, that is, the optimal solution R and T when the following formulas are minimized:

Figure 182643DEST_PATH_IMAGE031
Figure 182643DEST_PATH_IMAGE031

其中，

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为每组对应三维点的权重，求解时，通过奇异值分解的方法进行计算。in,
Figure 208368DEST_PATH_IMAGE032
is the weight of each group of corresponding three-dimensional points. When solving, it is calculated by the method of singular value decomposition.

S42、通过对应于每组标靶的转换参数，对与其布设位置相对应位置处的车载扫描系统的轨迹位置进行纠正。S42 , correcting the trajectory position of the vehicle-mounted scanning system at the position corresponding to the deployment position of each target group through the conversion parameters corresponding to each group of targets.

S421、根据每组标靶点云数据的激光扫描时刻，在待纠正的车载扫描系统的POS数据（车载扫描系统的位置姿态数据）中，确定扫描该组标靶时的车载扫描系统的轨迹位置

Figure 895308DEST_PATH_IMAGE001
。S421. According to the laser scanning time of each group of target point cloud data, in the POS data (position and attitude data of the vehicle-mounted scanning system) of the vehicle-mounted scanning system to be corrected, determine the trajectory position of the vehicle-mounted scanning system when scanning the group of targets
Figure 895308DEST_PATH_IMAGE001
.

由于原始的激光扫描点云时间分辨率（扫描频率）通常高于车载扫描系统轨迹的时间采样分辨率（采样频率），为保证每组标靶与车载扫描系统的轨迹位置一一对应。在本实施例的上述步骤中，针对各组标靶，根据组内各标靶对应的扫描时刻，在待纠正的车载扫描系统的POS数据中，查找与该标靶扫描时刻时间相邻的轨迹位置采样时刻：Since the time resolution (scanning frequency) of the original laser scanning point cloud is usually higher than the time sampling resolution (sampling frequency) of the trajectory of the vehicle scanning system, in order to ensure that each group of targets corresponds to the trajectory position of the vehicle scanning system one-to-one. In the above steps of this embodiment, for each group of targets, according to the scanning time corresponding to each target in the group, the POS data of the vehicle-mounted scanning system to be corrected is searched for the trajectory adjacent to the scanning time of the target. Location sampling time:

若存在标靶扫描时刻与对应轨迹位置采样时刻相同，则以该轨迹位置采样时刻所采样的轨迹位置，作为该组标靶对应的待纠正的车载扫描系统的轨迹位置

Figure 31891DEST_PATH_IMAGE001
，若组内多个标靶均存在标靶扫描时刻与对应轨迹位置采样时刻相同，则随机选取一个；If there is a target scanning time that is the same as the sampling time of the corresponding trajectory position, the trajectory position sampled at the trajectory position sampling time is used as the trajectory position of the vehicle-mounted scanning system to be corrected corresponding to the group of targets
Figure 31891DEST_PATH_IMAGE001
, if multiple targets in the group have the same target scanning time as the sampling time of the corresponding trajectory position, select one randomly;

若不存在标靶扫描时刻与对应轨迹位置采样时刻相同，则确定该标靶扫描时刻时间前后两侧的轨迹位置采样时刻，以满足于If there is no target scanning time that is the same as the sampling time of the corresponding trajectory position, then determine the trajectory position sampling time on both sides before and after the target scanning time, so as to satisfy the

Figure 169612DEST_PATH_IMAGE004
Figure 169612DEST_PATH_IMAGE004

的轨迹位置扫描时刻作为该标靶扫描时刻所对应的轨迹位置采样时刻，其中，

Figure 792004DEST_PATH_IMAGE005
为标靶扫描时刻，
Figure 31486DEST_PATH_IMAGE006
为轨迹位置采样时刻， s为轨迹位置数据的采样间隔时间；根据组内各标靶扫描时刻与其对应的轨迹位置采样时刻的差值，以差值最小的轨迹位置采样时刻所采样的轨迹位置，作为该组标靶对应的待纠正的车载扫描系统的轨迹位置
Figure 440602DEST_PATH_IMAGE001
。The scanning time of the trajectory position is taken as the sampling time of the trajectory position corresponding to the scanning time of the target, wherein,
Figure 792004DEST_PATH_IMAGE005
Scan time for the target,
Figure 31486DEST_PATH_IMAGE006
is the sampling time of the trajectory position, s is the sampling interval time of the trajectory position data; according to the difference between the scanning time of each target in the group and the sampling time of the corresponding trajectory position, the trajectory position sampled at the sampling time of the trajectory position with the smallest difference, The trajectory position of the vehicle-mounted scanning system to be corrected corresponding to the group of targets
Figure 440602DEST_PATH_IMAGE001
.

当然由于组内标靶的扫描始终存在一定的时间区间，因此，在该时间区间内的车载扫描系统的轨迹上的位置，均可以作为待纠正的车载扫描系统的轨迹位置

Figure 150938DEST_PATH_IMAGE001
，但应当注意取点的不同将影响转换精度。Of course, since the scanning of the targets in the group always has a certain time interval, the position on the trajectory of the vehicle-mounted scanning system in this time interval can be used as the trajectory position of the vehicle-mounted scanning system to be corrected.
Figure 150938DEST_PATH_IMAGE001
, but it should be noted that the difference in point selection will affect the conversion accuracy.

S422、通过对应组标靶的转换参数，对对应的待纠正车载扫描系统轨迹位置

Figure 315203DEST_PATH_IMAGE001
，利用S41计算得到的旋转矩阵R与平移矩阵T进行纠正，获得纠正后的车载扫描系统轨迹位置
Figure 963353DEST_PATH_IMAGE002
：S422. Through the conversion parameters of the corresponding group of targets, the corresponding trajectory position of the vehicle-mounted scanning system to be corrected
Figure 315203DEST_PATH_IMAGE001
, use the rotation matrix R and the translation matrix T calculated in S41 to correct, and obtain the corrected trajectory position of the vehicle-mounted scanning system
Figure 963353DEST_PATH_IMAGE002
:

Figure 189542DEST_PATH_IMAGE003
Figure 189542DEST_PATH_IMAGE003

S43、通过插值方法对相邻各组标靶间的车载扫描系统的轨迹位置进行补全，获取纠正后的车载扫描系统的整体移动轨迹。S43 , complementing the trajectory positions of the vehicle-mounted scanning systems between adjacent groups of targets through an interpolation method to obtain the corrected overall movement trajectory of the vehicle-mounted scanning system.

插值方法可以是现有的任意插值方法，但应当注意的是轨迹误差的累计特性和变形特点。根据惯性导航系统误差积累的特点，可认为相邻两组标靶间车载扫描系统的轨迹误差呈线性偏差状态，因此，在本实施例中，利用纠正后的相邻车载扫描系统的轨迹位置的三维坐标

Figure 567434DEST_PATH_IMAGE009
，通过线性内插方法对相邻各组标靶间的车载扫描系统的轨迹位置进行补全，具体包括：The interpolation method can be any existing interpolation method, but attention should be paid to the cumulative characteristics and deformation characteristics of trajectory errors. According to the error accumulation characteristics of the inertial navigation system, it can be considered that the trajectory error of the vehicle-mounted scanning system between two adjacent groups of targets is in a linear deviation state. Therefore, in this embodiment, the corrected trajectory position of the adjacent vehicle-mounted scanning system is used. 3D coordinates
Figure 567434DEST_PATH_IMAGE009
, the trajectory position of the vehicle-mounted scanning system between adjacent groups of targets is complemented by the linear interpolation method, including:

S431、根据相邻两组标靶所对应的轨迹位置的采样时刻

Figure 823971DEST_PATH_IMAGE007
，以及车载扫描系统的轨迹位置采样间隔时间 s，计算相邻车载扫描系统轨迹位置间的待纠正位置点个数n：S431, according to the sampling time of the trajectory positions corresponding to the adjacent two groups of targets
Figure 823971DEST_PATH_IMAGE007
, and the sampling interval s of the trajectory position of the on-board scanning system, and calculate the number n of the position points to be corrected between the trajectory positions of the adjacent on-board scanning system:

Figure 490576DEST_PATH_IMAGE008
Figure 490576DEST_PATH_IMAGE008

S432、通过纠正后的相邻车载扫描系统的轨迹位置的三维坐标

Figure 303811DEST_PATH_IMAGE009
，利用线性内插方法计算两点间n个待纠正点，获得纠正后的整体车载系统轨迹：S432, passing the corrected three-dimensional coordinates of the trajectory position of the adjacent vehicle-mounted scanning system
Figure 303811DEST_PATH_IMAGE009
, use the linear interpolation method to calculate n points to be corrected between two points, and obtain the corrected overall vehicle system trajectory:

Figure 490204DEST_PATH_IMAGE010
Figure 668376DEST_PATH_IMAGE011
Figure 490204DEST_PATH_IMAGE010
Figure 668376DEST_PATH_IMAGE011

Figure 71544DEST_PATH_IMAGE012
为第i个区间中的第j个待纠正点纠正前的位置坐标，
Figure 688470DEST_PATH_IMAGE033
为第i个区间中的第j个待纠正点纠正后的位置坐标，
Figure 455002DEST_PATH_IMAGE014
分别为对应采样时刻
Figure 335233DEST_PATH_IMAGE007
的纠正前的车载扫描系统轨迹位置坐标，
Figure 507588DEST_PATH_IMAGE009
分别为对应采样时刻
Figure 849577DEST_PATH_IMAGE007
的纠正后的车载扫描系统轨迹位置坐标。
Figure 71544DEST_PATH_IMAGE012
is the position coordinate of the jth point to be corrected in the ith interval before correction,
Figure 688470DEST_PATH_IMAGE033
is the corrected position coordinate of the jth point to be corrected in the ith interval,
Figure 455002DEST_PATH_IMAGE014
are the corresponding sampling times, respectively
Figure 335233DEST_PATH_IMAGE007
The coordinates of the vehicle scanning system trajectory position before correction,
Figure 507588DEST_PATH_IMAGE009
are the corresponding sampling times, respectively
Figure 849577DEST_PATH_IMAGE007
The corrected vehicle-mounted scanning system trajectory position coordinates.

步骤S5，隧道点云重生成，包括：Step S5, the tunnel point cloud is regenerated, including:

S51、将点云数据从激光扫描坐标

Figure 259829DEST_PATH_IMAGE015
转换到车载系统坐标
Figure 592853DEST_PATH_IMAGE034
：S51. Convert the point cloud data from the laser scanning coordinates
Figure 259829DEST_PATH_IMAGE015
Convert to vehicle system coordinates
Figure 592853DEST_PATH_IMAGE034
:

Figure 190188DEST_PATH_IMAGE017
Figure 190188DEST_PATH_IMAGE017

其中，

Figure 70288DEST_PATH_IMAGE018
表示激光扫描仪在车载系统坐标系中的姿态，
Figure 131785DEST_PATH_IMAGE019
表示激光扫描仪在车载系统坐标系中的位置，均为激光扫描仪的标定参数；in,
Figure 70288DEST_PATH_IMAGE018
represents the attitude of the laser scanner in the vehicle system coordinate system,
Figure 131785DEST_PATH_IMAGE019
Indicates the position of the laser scanner in the vehicle system coordinate system, which are the calibration parameters of the laser scanner;

S52、将点云数据从车载系统坐标

Figure 101621DEST_PATH_IMAGE034
转换到世界坐标
Figure 186252DEST_PATH_IMAGE020
，重新生成隧道点云数据：S52. Convert the point cloud data from the coordinates of the vehicle system
Figure 101621DEST_PATH_IMAGE034
Convert to world coordinates
Figure 186252DEST_PATH_IMAGE020
, regenerate the tunnel point cloud data:

Figure 683092DEST_PATH_IMAGE021
Figure 683092DEST_PATH_IMAGE021

其中，

Figure 51626DEST_PATH_IMAGE022
为车载坐标系到世界坐标系统的旋转矩阵，其误差不受GNSS缺失时间影响，无需进行改正，可由车载系统原始POS数据提供；
Figure 178982DEST_PATH_IMAGE023
由纠正后的车载系统POS数据提供，为车载坐标系到世界坐标系的平移参数，也即步骤S432中计算得到的轨迹纠正后位置
Figure 767220DEST_PATH_IMAGE033
；针对原始点云与车载系统轨迹位置的时间分辨率不一致的问题，以点云时间为基准，在轨迹位置数据两帧之间采用均匀采样的方法得到每个原始点云对应的轨迹姿态和位置。in,
Figure 51626DEST_PATH_IMAGE022
It is the rotation matrix from the vehicle coordinate system to the world coordinate system, and its error is not affected by the missing time of GNSS, so it does not need to be corrected, and can be provided by the original POS data of the vehicle system;
Figure 178982DEST_PATH_IMAGE023
Provided by the corrected on-board system POS data, it is the translation parameter from the on-board coordinate system to the world coordinate system, that is, the corrected position of the trajectory calculated in step S432
Figure 767220DEST_PATH_IMAGE033
; Aiming at the problem of inconsistent time resolution between the original point cloud and the trajectory position of the vehicle system, taking the point cloud time as the benchmark, the method of uniform sampling between the two frames of the trajectory position data is used to obtain the trajectory attitude and position corresponding to each original point cloud. .

需要说明的是，本发明中的“较大”、“较远”均是相对于阈值而言，即以相应的阈值作为比对基础，比如：某材料的反射率与隧道场景反射率的差异值大于一个阈值（可以根据经验设置），则称该材料的反射率与隧道场景反射率差异“较大”。It should be noted that "larger" and "farther" in the present invention are relative to the threshold, that is, the corresponding threshold is used as the basis for comparison, for example: the difference between the reflectivity of a material and the reflectivity of the tunnel scene A value greater than a threshold (which can be set empirically) is said to have a "large" difference between the reflectivity of the material and the reflectivity of the tunnel scene.

Claims (10)

1. The geometric correction method of the vehicle-mounted scanning point cloud under the tunnel GNSS rejection environment is characterized by comprising the following steps:
s1, performing on-site target layout in the tunnel region, wherein the targets are distributed in groups along the extending direction of the tunnel, each group comprises more than 4 targets, and the targets are arranged at intervals among the groups;
s2, mapping through a vehicle-mounted scanning system, and acquiring tunnel point cloud data including the point cloud data of each target; actually measuring the coordinates of the calibration points of each target through a tunnel measurement control network;
s3, processing the tunnel point cloud data, extracting corresponding target point cloud data in the point cloud data, and further acquiring corresponding homonymy point coordinates corresponding to the actually measured target calibration point in the target point cloud data according to the extracted target point cloud data;
s4, calculating conversion parameters corresponding to each group of targets through actually measured calibration point coordinates of each group of targets and homonymy point coordinates in target point cloud data corresponding to the calibration point coordinates, correcting the track position of the vehicle-mounted scanning system at the position corresponding to the arrangement position of each group of targets by using the conversion parameters corresponding to each group of targets respectively, and acquiring the corrected overall moving track of the vehicle-mounted scanning system;
and S5, resolving the tunnel point cloud data from the laser scanning coordinate system of the vehicle-mounted scanning system to a world coordinate system according to the corrected overall moving track of the vehicle-mounted scanning system, and regenerating tunnel point cloud.
2. The geometric correction method for the vehicle-mounted scanning point cloud under the GNSS rejection environment of the tunnel as claimed in claim 1, wherein in step S1, the distance between each group of targets along the extending direction of the tunnel is 200-1000 m, the arrangement length of each target in the group along the extending direction of the tunnel is less than 10m, and each target in the group is arranged on different horizontal planes; and the side length d of each target in the extending direction of the tunnel is greater than 0.08 x f/v, wherein f is the laser scanning frequency of the vehicle-mounted scanning system, and v is the driving speed of the vehicle-mounted scanning system.
3. The geometric correction method for the vehicle-mounted scanning point cloud under the tunnel GNSS rejection environment as claimed in claim 1, wherein the reflectivity of the material adopted for laying the target in the step S1 is higher than that of the tunnel scene, and the difference between the two reflectivities should satisfy the point cloud data extraction based on the intensity value; the shape of the target has shape characteristics required to satisfy shape-based point cloud target template matching.
4. The geometric correction method for the vehicle-mounted scanning point cloud under the tunnel GNSS denial environment according to claim 1, 2 or 3, wherein in step S3, the tunnel point cloud data is processed to extract corresponding target point cloud data in the point cloud data, and further obtain coordinates of a same-name point corresponding to the actual measurement target calibration point in each target point cloud data according to the extracted target point cloud data, specifically comprising:
s31, intensity sampling is carried out on the point cloud data: randomly selecting part of point cloud data of a target from point cloud data acquired by a vehicle-mounted scanning system to perform intensity value sampling, and determining the intensity value range of the point cloud data of the target;
s32, performing information enhancement on the point cloud data: stretching the intensity value of the point cloud data through the intensity value range of the target point cloud data determined in the step S31;
s33, coarse extraction of target point cloud data; performing histogram statistics on the point cloud data enhanced in the step S32, and eliminating other point cloud data different from the determined target point cloud data intensity value range in the point cloud data according to the intensity information to obtain roughly extracted target point cloud data;
s34, carrying out shape matching on the target point cloud data obtained by crude extraction and a prefabricated standard point cloud target template to obtain target point cloud data extracted precisely;
and S35, extracting the coordinates of the homonymy points corresponding to the actual measurement target calibration points in the target point cloud data according to the target point cloud data extracted precisely.
5. The geometric correction method for the vehicle-mounted scanning point cloud under the tunnel GNSS denial environment according to claim 1, 2 or 3, wherein the step S4 specifically includes:
s41, respectively calculating conversion parameters of the coordinates of each actually measured calibration point and the coordinates of the point cloud homonymous point corresponding to the actually measured calibration point through the coordinates of the actually measured calibration points of each group of targets and the coordinates of the homonymous point in the point cloud data of the targets corresponding to the actually measured calibration point;
s42, correcting the track position of the vehicle-mounted scanning system at the position corresponding to the arrangement position of each group of targets through the conversion parameters corresponding to each group of targets;
and S43, completing the track positions of the vehicle-mounted scanning systems between the adjacent groups of targets through an interpolation method, and acquiring the corrected overall moving track of the vehicle-mounted scanning systems.
6. The method for geometry correction of vehicle-mounted scanning point cloud under tunnel GNSS rejection environment according to claim 5, wherein in step S41, said calculating the conversion parameters of each set of measured calibration point coordinates and the point cloud homonymy point coordinates corresponding thereto specifically includes: calculating the coordinate of each set of actually measured calibration points and the rotation matrix of the point cloud homonymous point coordinate corresponding to the actually measured calibration point coordinate by a singular value decomposition methodRAnd translation matrixT。
7. The geometric correction method for the vehicle-mounted scanning point cloud under the GNSS rejection environment of the tunnel according to claim 6, wherein in step S42, the correcting the track position of the vehicle-mounted scanning system at the position corresponding to the layout position thereof by the conversion parameters corresponding to each set of targets specifically comprises:
s421, according to the laser scanning time of each group of target point cloud data, determining the track position of the vehicle-mounted scanning system when the group of targets are scanned in the POS data of the vehicle-mounted scanning system to be corrected
Figure 924378DEST_PATH_IMAGE001
；
S422, the track position of the corresponding vehicle-mounted scanning system to be corrected is corrected through the conversion parameters of the corresponding group of targets
Figure 140727DEST_PATH_IMAGE001
Using a rotation matrixRAnd translation matrixTCorrecting to obtain the corrected track position of the vehicle-mounted scanning system
Figure 461987DEST_PATH_IMAGE002
：
Figure 13054DEST_PATH_IMAGE003
。
8. The geometric correction method for the vehicle-mounted scanning point cloud under the tunnel GNSS denial environment according to claim 5, wherein in step S42, for each group of targets, according to the scanning time corresponding to each target in the group, in the POS data of the vehicle-mounted scanning system to be corrected, the track position sampling time adjacent to the target scanning time is searched:
if the target scanning time is the same as the sampling time of the corresponding track position, taking the track position sampled at the sampling time of the track position as the track position of the vehicle-mounted scanning system to be corrected corresponding to the group of targets
Figure 297536DEST_PATH_IMAGE001
If the target scanning time of a plurality of targets in the group is the same as the sampling time of the corresponding track position, one target is randomly selected;
if the target scanning time is not the same as the corresponding track position sampling time, determining track position sampling times at the front side and the rear side of the target scanning time so as to meet the requirement of
Figure 185636DEST_PATH_IMAGE004
The scanning time of the track position of the target is taken as the sampling time of the track position corresponding to the scanning time of the target, wherein,
Figure 412218DEST_PATH_IMAGE005
in order to be the moment of scanning of the target,
Figure 185002DEST_PATH_IMAGE006
sampling time of the track position, and s is sampling interval time of track position data; according to the difference value between the scanning time of each target in the group and the sampling time of the corresponding track position, the track position sampled at the sampling time of the track position with the minimum difference value is used as the track position of the vehicle-mounted scanning system to be corrected corresponding to the group of targets
Figure 273175DEST_PATH_IMAGE001
。
9. The method as claimed in claim 5, wherein in step S43, the method for geometrically correcting the vehicle-mounted scanning point cloud under the GNSS denial environment of the tunnel includes the steps of:
s431, sampling time according to track positions corresponding to two adjacent groups of targets
Figure 978963DEST_PATH_IMAGE007
And the track position sampling interval time s of the vehicle-mounted scanning system, and calculating the number n of position points to be corrected between the track positions of the adjacent vehicle-mounted scanning systems:
Figure 861600DEST_PATH_IMAGE008
s432, passing the corrected three-dimensional coordinates of the track positions of the adjacent vehicle-mounted scanning systems
Figure 387259DEST_PATH_IMAGE009
Calculating n points to be corrected between the two points by using a linear interpolation method to obtain the corrected track of the whole vehicle-mounted system:
Figure 262811DEST_PATH_IMAGE010
Figure 308258DEST_PATH_IMAGE011
Figure 611064DEST_PATH_IMAGE012
for the position coordinate before the correction of the jth point to be corrected in the ith interval,
Figure 434139DEST_PATH_IMAGE013
for the corrected position coordinate of the jth point to be corrected in the ith interval,
Figure 378961DEST_PATH_IMAGE014
are respectively corresponding sampling time
Figure 278915DEST_PATH_IMAGE007
The position coordinates of the track of the vehicle-mounted scanning system before correction,
Figure 18201DEST_PATH_IMAGE009
are respectively corresponding sampling time
Figure 269185DEST_PATH_IMAGE007
And (4) correcting the track position coordinates of the vehicle-mounted scanning system.
10. The method for geometrically correcting the cloud of vehicle-mounted scanning points under the GNSS rejection environment of the tunnel according to claim 9, wherein the step S5 specifically includes:
s51, scanning the point cloud data from the laser
Figure 220960DEST_PATH_IMAGE015
Conversion to vehicle system coordinates
Figure 755847DEST_PATH_IMAGE016
：
Figure 151187DEST_PATH_IMAGE017
Wherein,
Figure 873156DEST_PATH_IMAGE018
representing the pose of the laser scanner in the vehicle system coordinate system,
Figure 907583DEST_PATH_IMAGE019
the positions of the laser scanners in the coordinate system of the vehicle-mounted system are represented, and are calibration parameters of the laser scanners;
s52, the point cloud data is converted from the coordinates of the vehicle-mounted system
Figure 31397DEST_PATH_IMAGE016
Conversion to world coordinates
Figure 112486DEST_PATH_IMAGE020
And regenerating tunnel point cloud data:
Figure 72482DEST_PATH_IMAGE021
wherein,
Figure 631640DEST_PATH_IMAGE022
a rotation matrix from the vehicle-mounted coordinate system to the world coordinate system is provided by the original POS data of the vehicle-mounted system,
Figure 360692DEST_PATH_IMAGE023
the translation parameter from the vehicle-mounted coordinate system to the world coordinate system, that is, the corrected position of the track calculated in step S432
Figure 878261DEST_PATH_IMAGE013
；
And obtaining the track attitude and position corresponding to each original point cloud by adopting a uniform sampling method between two frames of track position data.
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