LiDAR ranging angle measurement calibration method in mobile robot

Zhao Haipeng; Du Yuhong; Ding Juan; Zhao Di; Shi Yijun

doi:10.3788/IRLA201948.0630002

Volume 48 Issue 6

Jul. 2019

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Article Navigation > Infrared and Laser Engineering > 2019 > 48(6): 630002-0630002(8)

Zhao Haipeng, Du Yuhong, Ding Juan, Zhao Di, Shi Yijun. LiDAR ranging angle measurement calibration method in mobile robot[J]. Infrared and Laser Engineering, 2019, 48(6): 630002-0630002(8). doi: 10.3788/IRLA201948.0630002

Citation:

Zhao Haipeng, Du Yuhong, Ding Juan, Zhao Di, Shi Yijun. LiDAR ranging angle measurement calibration method in mobile robot[J]. Infrared and Laser Engineering, 2019, 48(6): 630002-0630002(8). doi: 10.3788/IRLA201948.0630002

LiDAR ranging angle measurement calibration method in mobile robot

doi: 10.3788/IRLA201948.0630002

1.
School of Mechanical Engineering,Tianjin Polytechnic University,Tianjin 300387,China;
2.
Tianjin Key Laboratory of Modern Mechanical and Electrical Equipment Technology,Tianjin 300387,China;
3.
Tianjin Zhonghuan Electronic Computer Co.,Ltd.,Tianjin 300190,China

Received Date: 2019-01-10
Rev Recd Date: 2019-02-20
Publish Date: 2019-06-25

Abstract

Aiming at the problem that the current mobile robots have low accuracy for the construction of environmental maps, the calibration methods of ranging and angle measurement of LiDAR were proposed respectively. The error propagation law was used to analyze the ranging error factor of the LiDAR. It can be seen that the LiDAR ranging error was mainly caused by the echo intensity and the measuring distance, and the ranging error correction model was derived. By analyzing the error factors of LiDAR angle measurement, a triangulation calibration method was proposed for the error caused by the eccentricity of the mechanical scanning axis and the geometric rotation center, and the angle error correction model was established. The mobile robot coordinate conversion system was modified according to the LiDAR ranging and the angle correction model. The experimental results show that the standardization of the distance measurement increases the standard deviation of the longitudinal coordinate difference of the plane obstacle data by 30%-60%, which is close to the real geometric feature of the object. The angle measurement method improves the coincidence effect of the obstacle data by 30%. The accuracy of map construction of mobile robots is improved using the calibration method.
- LiDAR,
- calibration method,
- echo intensity,
- correction model

References

[1]	Singh N H, Thongam K. Mobile robot navigation using MLP-BP approaches in dynamic environments[J]. Arabian Journal for Science Engineering, 2018, 43:1-16.
[2]	Lamini C, Benhlima S, Elbekri A. Genetic algorithm based approach for autonomous mobile robot path planning[J]. Procedia Computer Science, 2018, 127:180-189.
[3]	Jian M, Tang M, Zhang C, et al. Indoor mobile robot map construction based on improved linear feature extraction algorithm[J]. Computer Engineering, 2018, 44(1):23-29.
[4]	Chen Q. Research on simultaneous localization and map construction of mobile robot based on adaptive method[J]. Microcomputer Applications, 2018, 3:24-28.
[5]	Han Wei. Autonomous navigation of multi-sensor based crawler robot in outdoor environment[D]. Dalian:Dalian University of Technology, 2017. (in Chinese) 韩伟. 室外环境基于多传感器履带式机器人自主导航[D]. 大连:大连理工大学, 2017.
[6]	Xiang Zhiyu. Fast 3D scanning laser radar system design and calibration[J]. Journal of Zhejiang University(Engineering Science), 2006, 40(12):2130-2133. (in Chinese) 项志宇. 快速三维扫描激光雷达的设计及其系统标定[J]. 浙江大学学报(工学版), 2006, 40(12):2130-2133.
[7]	Gielsdorf F, Rietdorf A, Gruendig L. A concept for the calibration of terrestrial laser scanners[C]//Proceedings FIG Working Week (on CD-ROM). Athens, Greece:International Federation of Surveyors, 2004.
[8]	Reshetyuk Y. Investigation and calibration of pulsed time-of-flight terrestrial laser scanners[D]. Stockholm:Royal Institute of Technology(KTH), 2006.
[9]	Yu Jinxia, Cai Zixing, Zou Xiaobing, et al. Research on ranging performance of laser scanner in the navigation of mobile robot[J]. Chinese Journal of Sensors and Actuators, 2006, 19(2):356-360. (in Chinese) 于金霞, 蔡自兴, 邹小兵,等. 移动机器人导航中激光雷达测距性能研究[J]. 传感技术学报, 2006, 19(2):356-360.
[10]	Lindner M, Schiller I, Kolb A, et al. Time-of-Flight sensor calibration for accurate range sensing[J]. Computer Vision Image Understanding, 2010, 114(12):1318-1328.
[11]	Ma Hao. Research of error calibration method of airborne lidar system[D]. Jinan:Shandong University of Science and Technology, 2011. (in Chinese) 马浩. 机载激光雷达测量系统误差检校方法研究[D]. 济南:山东科技大学, 2011.
[12]	Wang Guining, Liu Bingyi, Feng Changzhong, et al. Data quality control method for VAD wind field retrieval based on coherent wind lidar[J]. Infrared and Laser Engineering, 2018, 47(2):0230002. (in Chinese) 王贵宁, 刘秉义, 冯长中,等. 相干测风激光雷达VAD风场反演的数据质量控制方法[J]. 红外与激光工程, 2018, 47(2):0230002.
[13]	Du Yuhong, Wei Kunpeng, Shi Yijun, et al. Infrared detection and clustering grey fusion prediction model of water quality turbidity[J]. Infrared and Laser Engineering, 2016, 45(10):1028002. (in Chinese) 杜玉红,魏坤鹏, 史屹君,等. 水质浊度红外光检测及聚类灰色融合预测模型[J]. 红外与激光工程, 2016, 45(10):1028002.
[14]	Du Yuhong, Ma Ting, Yang Chengwu, et al. Detection clustering analysis algorithm and system parameters study of the near-point multi-class foreign fiber[J]. Journal of the Textile Institute, 2017, 108(6):1022-1027.
[15]	Luo Le, Wu Changqiang, Lin Jie, et al. Time-domain denoising based on photon-counting LiDAR[J]. Optics and Precision Engineering, 2018, 26(5):1175-1180. (in Chinese) 骆乐, 吴长强, 林杰,等. 基于光子计数激光雷达的时域去噪[J]. 光学精密工程, 2018, 26(5):1175-1180.
[16]	Yang Juqing, Wang Dayong, Dong Dengfeng, et al. Laser measurement based evaluation for orthogonal transformation calibration of robot pose[J]. Optics and Precision Engineering, 2018, 26(8):1985-1993. (in Chinese) 杨聚庆, 王大勇, 董登峰,等. 激光测量标定机器人坐标系位姿变换的正交化解算方法[J]. 光学精密工程, 2018, 26(8):1985-1993.
[17]	Bu Yuming, Du Xiaoping, Zeng Zhaoyang, et al. Development and trend analysis of scannerless 3D imaging ladar[J]. Chinese Optics, 2018, 11(5):711-727. 卜禹铭, 杜小平, 曾朝阳,等. 无扫描激光三维成像雷达研究进展及趋势分析[J]. 中国光学, 2018, 11(5):711-727.
[18]	Wen Ya, Wu Chunting, Yuan Zerui, et al. Research progress of far-infrared solid state lasers[J]. Chinese Optics, 2018, 11(6):889-900. (in Chinese) 温雅, 吴春婷, 袁泽锐, 等. 远红外固体激光器研究进展[J]. 中国光学, 2018, 11(6):889-900.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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LiDAR ranging angle measurement calibration method in mobile robot

1. School of Mechanical Engineering,Tianjin Polytechnic University,Tianjin 300387,China;

2. Tianjin Key Laboratory of Modern Mechanical and Electrical Equipment Technology,Tianjin 300387,China;

3. Tianjin Zhonghuan Electronic Computer Co.,Ltd.,Tianjin 300190,China

Received Date: 2019-01-10

Rev Recd Date: 2019-02-20

Publish Date: 2019-06-25

Key words:

Abstract: Aiming at the problem that the current mobile robots have low accuracy for the construction of environmental maps, the calibration methods of ranging and angle measurement of LiDAR were proposed respectively. The error propagation law was used to analyze the ranging error factor of the LiDAR. It can be seen that the LiDAR ranging error was mainly caused by the echo intensity and the measuring distance, and the ranging error correction model was derived. By analyzing the error factors of LiDAR angle measurement, a triangulation calibration method was proposed for the error caused by the eccentricity of the mechanical scanning axis and the geometric rotation center, and the angle error correction model was established. The mobile robot coordinate conversion system was modified according to the LiDAR ranging and the angle correction model. The experimental results show that the standardization of the distance measurement increases the standard deviation of the longitudinal coordinate difference of the plane obstacle data by 30%-60%, which is close to the real geometric feature of the object. The angle measurement method improves the coincidence effect of the obstacle data by 30%. The accuracy of map construction of mobile robots is improved using the calibration method.

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Reference (18)

[1]	Singh N H, Thongam K. Mobile robot navigation using MLP-BP approaches in dynamic environments[J]. Arabian Journal for Science Engineering, 2018, 43:1-16.
[2]	Lamini C, Benhlima S, Elbekri A. Genetic algorithm based approach for autonomous mobile robot path planning[J]. Procedia Computer Science, 2018, 127:180-189.
[3]	Jian M, Tang M, Zhang C, et al. Indoor mobile robot map construction based on improved linear feature extraction algorithm[J]. Computer Engineering, 2018, 44(1):23-29.
[4]	Chen Q. Research on simultaneous localization and map construction of mobile robot based on adaptive method[J]. Microcomputer Applications, 2018, 3:24-28.
[5]	Han Wei. Autonomous navigation of multi-sensor based crawler robot in outdoor environment[D]. Dalian:Dalian University of Technology, 2017. (in Chinese) 韩伟. 室外环境基于多传感器履带式机器人自主导航[D]. 大连:大连理工大学, 2017.
[6]	Xiang Zhiyu. Fast 3D scanning laser radar system design and calibration[J]. Journal of Zhejiang University(Engineering Science), 2006, 40(12):2130-2133. (in Chinese) 项志宇. 快速三维扫描激光雷达的设计及其系统标定[J]. 浙江大学学报(工学版), 2006, 40(12):2130-2133.
[7]	Gielsdorf F, Rietdorf A, Gruendig L. A concept for the calibration of terrestrial laser scanners[C]//Proceedings FIG Working Week (on CD-ROM). Athens, Greece:International Federation of Surveyors, 2004.
[8]	Reshetyuk Y. Investigation and calibration of pulsed time-of-flight terrestrial laser scanners[D]. Stockholm:Royal Institute of Technology(KTH), 2006.
[9]	Yu Jinxia, Cai Zixing, Zou Xiaobing, et al. Research on ranging performance of laser scanner in the navigation of mobile robot[J]. Chinese Journal of Sensors and Actuators, 2006, 19(2):356-360. (in Chinese) 于金霞, 蔡自兴, 邹小兵,等. 移动机器人导航中激光雷达测距性能研究[J]. 传感技术学报, 2006, 19(2):356-360.
[10]	Lindner M, Schiller I, Kolb A, et al. Time-of-Flight sensor calibration for accurate range sensing[J]. Computer Vision Image Understanding, 2010, 114(12):1318-1328.
[11]	Ma Hao. Research of error calibration method of airborne lidar system[D]. Jinan:Shandong University of Science and Technology, 2011. (in Chinese) 马浩. 机载激光雷达测量系统误差检校方法研究[D]. 济南:山东科技大学, 2011.
[12]	Wang Guining, Liu Bingyi, Feng Changzhong, et al. Data quality control method for VAD wind field retrieval based on coherent wind lidar[J]. Infrared and Laser Engineering, 2018, 47(2):0230002. (in Chinese) 王贵宁, 刘秉义, 冯长中,等. 相干测风激光雷达VAD风场反演的数据质量控制方法[J]. 红外与激光工程, 2018, 47(2):0230002.
[13]	Du Yuhong, Wei Kunpeng, Shi Yijun, et al. Infrared detection and clustering grey fusion prediction model of water quality turbidity[J]. Infrared and Laser Engineering, 2016, 45(10):1028002. (in Chinese) 杜玉红,魏坤鹏, 史屹君,等. 水质浊度红外光检测及聚类灰色融合预测模型[J]. 红外与激光工程, 2016, 45(10):1028002.
[14]	Du Yuhong, Ma Ting, Yang Chengwu, et al. Detection clustering analysis algorithm and system parameters study of the near-point multi-class foreign fiber[J]. Journal of the Textile Institute, 2017, 108(6):1022-1027.
[15]	Luo Le, Wu Changqiang, Lin Jie, et al. Time-domain denoising based on photon-counting LiDAR[J]. Optics and Precision Engineering, 2018, 26(5):1175-1180. (in Chinese) 骆乐, 吴长强, 林杰,等. 基于光子计数激光雷达的时域去噪[J]. 光学精密工程, 2018, 26(5):1175-1180.
[16]	Yang Juqing, Wang Dayong, Dong Dengfeng, et al. Laser measurement based evaluation for orthogonal transformation calibration of robot pose[J]. Optics and Precision Engineering, 2018, 26(8):1985-1993. (in Chinese) 杨聚庆, 王大勇, 董登峰,等. 激光测量标定机器人坐标系位姿变换的正交化解算方法[J]. 光学精密工程, 2018, 26(8):1985-1993.
[17]	Bu Yuming, Du Xiaoping, Zeng Zhaoyang, et al. Development and trend analysis of scannerless 3D imaging ladar[J]. Chinese Optics, 2018, 11(5):711-727. 卜禹铭, 杜小平, 曾朝阳,等. 无扫描激光三维成像雷达研究进展及趋势分析[J]. 中国光学, 2018, 11(5):711-727.
[18]	Wen Ya, Wu Chunting, Yuan Zerui, et al. Research progress of far-infrared solid state lasers[J]. Chinese Optics, 2018, 11(6):889-900. (in Chinese) 温雅, 吴春婷, 袁泽锐, 等. 远红外固体激光器研究进展[J]. 中国光学, 2018, 11(6):889-900.

LiDAR ranging angle measurement calibration method in mobile robot

doi: 10.3788/IRLA201948.0630002

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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