地面式全景激光扫描仪设计及其标定与数据融合方法

doi:10.3778/j.issn.1002-8331.2501-0080

摘要/Abstract

摘要： 地面式激光扫描仪作为一种实时的高精度三维重建工具，在三维重建和智能机器人和测绘等领域有着重要应用，因其价格昂贵，难以广泛应用。使用消费级激光雷达和相机研制的激光扫描仪能够在成本相对较低的情况下同样实现场景的三维重建，面临传感器相对位姿标定和异构传感器数据融合等问题。使用基于点云边缘线特征匹配的方法和基于强度-灰度归一化信息距离优化的方法分别完成激光雷达与电机、相机之间的外参标定，同时对扫描结果进行了后处理并提出了一种基于像素距离加权的点云着色融合算法，改进最终的扫描点云质量。实验结果表明，研制的地面式全景激光扫描仪测量精度能够保持在所使用激光雷达的原测量精度范围内（1 cm~2 cm），扫描结果能够满足一般场景下的全景三维重建要求。

关键词: 激光雷达, 相机, 外参标定, 传感器融合, 点云处理

Abstract: Terrestrial laser scanners, serving as real-time and high-precision three-dimensional reconstruction tools, play a significant role in various fields such as intelligent robotics and surveying. However, their high cost has limited their widespread application. Laser scanners developed with consumer-grade lidar and cameras offer a more affordable alternative for achieving three-dimensional scene reconstruction. Nevertheless, this approach encounters challenges like the calibration of relative sensor poses and the fusion of heterogeneous sensor data. In this study, the extrinsic calibration between the lidar and the motor and camera, is accomplished using a method based on point cloud edge line feature matching and another method that optimizes the intensity-gray normalized information distance. Additionally, post-processing is performed on the scanning results, and a point cloud coloring fusion algorithm based on pixel distance weighting is proposed to enhance the quality of the final scanned point cloud. The experimental findings indicate that the measurement accuracy of the developed terrestrial panoramic laser scanner can be maintained within the original accuracy range (1 cm-2 cm) of the utilized lidar, and the scanning results can fulfill the requirements for panoramic three-dimensional reconstruction in common scenarios.

Key words: LiDAR, camera, extrinsic calibration, laser ranging, sensor fusion, point cloud processing

彭鹏霏, 燕玉林, 仲训昱, 王宁宁. 地面式全景激光扫描仪设计及其标定与数据融合方法[J]. 计算机工程与应用, 2025, 61(24): 206-215.

PENG Pengfei, YAN Yulin, ZHONG Xunyu, WANG Ningning. Design, Calibration and Data Fusion Methods of Terrestrial Panoramic Laser Scanners[J]. Computer Engineering and Applications, 2025, 61(24): 206-215.

参考文献

[1] TANG P B, HUBER D, AKINCI B, et al. Automatic reconstruction of as-built building information models from laser-scanned point clouds: a review of related techniques[J]. Automation in Construction, 2010, 19(7): 829-843.
[2] LI Y, IBANEZ-GUZMAN J. Lidar for autonomous driving: the principles, challenges, and trends for automotive lidar and perception systems[J]. IEEE Signal Processing Magazine, 2020, 37(4): 50-61.
[3] 代领, 宋振波, 陆建峰. 用于SLAM的点云动态物体识别[J]. 计算机工程与应用, 2024, 60(20): 312-319.
DAI L, SONG Z B, LU J F. Point cloud dynamic object recognition for SLAM[J]. Computer Engineering and Applications, 2024, 60(20): 312-319.
[4] 刘铭哲, 徐光辉, 唐堂, 等. 激光雷达SLAM算法综述[J]. 计算机工程与应用, 2024, 60(1): 1-14.
LIU M Z, XU G H, TANG T, et al. Review of SLAM based on lidar[J]. Computer Engineering and Applications, 2024, 60(1): 1-14.
[5] BI S S, YUAN C, LIU C, et al. A survey of low-cost 3D laser scanning technology[J]. Applied Sciences, 2021, 11(9): 3938.
[6] BULA J, DERRON M H, MARIETHOZ G. Dense point cloud acquisition with a low-cost Velodyne VLP-16[J]. Geoscientific Instrumentation, Methods and Data Systems, 2020, 9(2): 385-396.
[7] NEUMANN T, FERREIN A, KALLWEIT S, et al. Towards a mobile mapping robot for underground mines[C]//Proceedings of the 2014 International Joint Symposium on RobMech and AfLaT, 2014.
[8] 汪双. 基于旋转激光雷达的融合定位建图方法[D]. 绵阳: 西南科技大学, 2024.
WANG S. The method of fusion localization and mapping based on spinning[D]. Mianyang: Southwest University of Science and Technology, 2024.
[9] B?DKOWSKI J, PE?KA M. Affordable robotic mobile mapping system based on lidar with additional rotating planar reflector[J]. Sensors, 2023, 23(3): 1551.
[10] 吕佳俊, 郎晓磊, 李宝润, 等. 基于B样条的连续时间轨迹状态估计研究综述[J]. 机器人, 2024, 46(6): 743-752.
Lü J J, LANG X L, LI B R, et al. Review of continuous-time trajectory state estimation research based on B-splines[J]. Robot, 2024, 46(6): 743-752.
[11] LI X C, XIAO Y X, WANG B B, et al. Automatic targetless LiDAR camera calibration: a survey[J]. Artificial Intelligence Review, 2023, 56(9): 9949-9987.
[12] NEITZEL F. Investigation of axes errors of terrestrial laser scanners [C]//Proceedings of the 5th International Symposium Turkish-German Joint Geodetic Days, 2006.
[13] YUAN C J, LIU X Y, HONG X P, et al. Pixel-level extrinsic self calibration of high resolution LiDAR and camera in targetless environments[J]. IEEE Robotics and Automation Letters, 2021, 6(4): 7517-7524.
[14] 隋心, 王思语, 罗力, 等. 基于点云特征的改进RANSAC地面分割算法[J]. 导航定位学报, 2024, 12(1): 106-114.
SUI X, WANG S Y, LUO L, et al. Improved RANSAC ground segmentation algorithm based on point cloud features[J]. Journal of Navigation and Positioning, 2024, 12(1): 106-114.
[15] KOIDE K, OISHI S, YOKOZUKA M, et al. General, single-shot, target-less, and automatic LiDAR-camera extrinsic calibration toolbox[C]//Proceedings of the 2023 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2023: 11301-11307.
[16] PENG H C, LONG F H, DING C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(8): 1226-1238.
[17] ALEXA M, BEHR J, COHEN-OR D, et al. Computing and rendering point set surfaces[J]. IEEE Transactions on Visualization and Computer Graphics, 2003, 9(1): 3-15.
[18] ZHANG J C, GAO Y, XU Y, et al. A simple yet effective image stitching with computational suture zone[J]. The Visual Computer, 2023, 39(10): 4915-4928.
[19] FOOTE T. Tf: the transform library[C]//Proceedings of the 2013 IEEE Conference on Technologies for Practical Robot Applications. Piscataway: IEEE, 2013: 1-6.
[20] ZHANG Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.