LIDAR data segmentation method adapting to environmental characteristics

Du Yuhong; Wang Peng; Shi Yijun; Wang Luyao; Zhao Di

doi:10.3788/IRLA201847.0830001

Volume 47 Issue 8

Aug. 2018

Turn off MathJax

Article Contents

Article Navigation > Infrared and Laser Engineering > 2018 > 47(8): 830001-0830001(8)

Du Yuhong, Wang Peng, Shi Yijun, Wang Luyao, Zhao Di. LIDAR data segmentation method adapting to environmental characteristics[J]. Infrared and Laser Engineering, 2018, 47(8): 830001-0830001(8). doi: 10.3788/IRLA201847.0830001

Citation:

Du Yuhong, Wang Peng, Shi Yijun, Wang Luyao, Zhao Di. LIDAR data segmentation method adapting to environmental characteristics[J]. Infrared and Laser Engineering, 2018, 47(8): 830001-0830001(8). doi: 10.3788/IRLA201847.0830001

LIDAR data segmentation method adapting to environmental characteristics

doi: 10.3788/IRLA201847.0830001

Du Yuhong^1,2,
Wang Peng^1,2,
Shi Yijun³,
Wang Luyao^1,2,
Zhao Di^1,2

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.
The Technology Center of Tianjin Zhonghuan Creative Technology Limited,Tianjin 300190,China

Received Date: 2018-03-10
Rev Recd Date: 2018-04-20
Publish Date: 2018-08-25

Abstract

In order to solve the problem that LIDAR data segmentation algorithm cannot adapt to the environmental characteristics and determine the threshold continuously and accurately, an adaptive LIDAR data segmentation algorithm based on environmental features was proposed. According to the data characteristics of the two-dimensional lidar and the geometric characteristics of the indoor environment, the virtual environment line was fitted with the adjacent point of the laser radar data. The intersection of the virtual environment line and the adjacent laser scanning ray was taken as the reference point to determine the adaptive threshold pre-segmentation of radar data. In view of the defects in the data pre segmentation results completed by the above method, a method for judging pseudo breakpoints after data pre segmentation was proposed, and the algorithm was optimized. The algorithm was compared and analyzed with piecewise threshold segmentation algorithm and linear equation threshold segmentation algorithm. The LIDAR data segmentation algorithm adapting to environmental characteristics achieves a successful segmentation rate of 98% for the experimental data, and has better environment adaptability and higher segmentation accuracy.
- data segmentation,
- LIDAR,
- robot,
- environmental characteristics

References

[1]	An S Y, Lee L K, Oh S Y. Ceiling vision-based active SLAM framework for dynamic and wide-open environments[J]. Autonomous Robots, 2016, 40(2):291-324.
[2]	Paneru B, Basnet N, Shrestha S, et al. Autonomous navigation of a mobile robot in indoor environments[J]. Zerone Scholar, 2016, 1(1):3-8.
[3]	Torres-Gonzlez A, Martnez-de Dios J R, Ollero A. Robot-beacon distributed range-only SLAM for resource-constrained operation[J]. Sensors, 2017, 17(4):903-921.
[4]	Koch P, May S, Schmidpeter M, et al. Multi-robot localization and mapping based on signed distance functions[J]. Journal of Intelligent Robotic Systems, 2016, 83(3-4):409-428.
[5]	Schaefer A, Luft L, Burgard W. An analytical lidar sensor model based on ray path information[J]. IEEE Robotics and Automation Letters, 2017, 2(3):1405-1412.
[6]	Gallant M J, Marshall J A. Two-dimensional axis mapping using LiDAR[J]. IEEE Transactions on Robotics, 2016, 32(1):150-160.
[7]	Misu K, Miura J. Specific person tracking using 3D LIDAR and ESPAR antenna for mobile service robots[J]. Advanced Robotics, 2015, 29(22):1483-1495.
[8]	Nguyen V, Gchter S, Martinelli A, et al. A comparison of line extraction algorithms using 2D range data for indoor mobile robotics[J]. Autonomous Robots, 2007, 23(2):97-111.
[9]	Borges G A, Aldon M J. Line extraction in 2D range images for mobile robotics[J]. Journal of Intelligent Robotic Systems, 2004, 40(3):267-297.
[10]	Lee M, Hur S, Park Y. Obstacle classification method using multi feature comparison based on single 2D LiDAR[J]. Journal of Institute of Control, Robotics and Systems, 2016, 22(4):253-265.
[11]	Wang W, Sakurada K, Kawaguchi N. Incremental and enhanced scanline-based segmentation method for surface reconstruction of sparse LiDAR data[J]. Remote Sensing, 2016, 8(11):967.
[12]	Li Junze. Research on indoor robot SLAM based on LIDAR[D]. Guangzhou:South China University of Technology,2016. (in Chinese)李昀泽. 基于激光雷达的室内机器人SLAM研究[D]. 广州:华南理工大学, 2016.
[13]	Hang Yijun, Liu Jianye, Li Rongbing, et al. MEMS IMU/LADAR integrated navigation method based on mixed feature match[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(9):2583-2592. (in Chinese)杭义军, 刘建业, 李荣冰, 等. 基于混合特征匹配的微惯性/激光雷达组合导航方法[J]. 航空学报, 2014, 35(9):2583-2592.
[14]	Cao Qiusheng. Technical discussion on imaging lidars as UAV payloads[J]. Infrared and Laser Engineering, 2016, 45(10):1003002. (in Chinese)曹秋生. 成像激光雷达的无人机载技术探讨[J]. 红外与激光工程, 2016, 45(10):1003002.
[15]	Shi Fengdong, Liu Wenhao, Wang Xin, et al. Design of indoor laser radar navigation system[J]. Infrared and Laser Engineering, 2015, 44(12):3570-3575. (in Chinese)史风栋, 刘文皓, 汪鑫, 等. 室内激光雷达导航系统设计[J]. 红外与激光工程, 2015, 44(12):3570-3575.
[16]	Liu Zhiqing, Li Pengcheng, Chen Xiaowei, et al. Classification of airborne LiDAR point cloud data based on information vector machine[J]. Optics and Precision Engineering, 2016, 24(1):210-219. (in Chinese)刘志青, 李鹏程, 陈小卫, 等. 基于信息向量机的机载激光雷达点云数据分类[J]. 光学精密工程, 2016, 24(1):210-219.
[17]	Yang Zijian, Chen Feng, Li Chao, et al. Transient effect of dead time of photon-counting in micro-pulse lidar[J]. Optics and Precision Engineering, 2015, 23(2):408-414. (in Chinese)杨子健, 陈锋, 李抄, 等. 微脉冲激光雷达中的光子计数死区时间瞬态效应[J]. 光学精密工程, 2015, 23(2):408-414.
[18]	Yan Jie, Ruan Youtian, Xue Peiyao. Active and passive optical image fusion technology[J]. Chinese Optics, 2015,8(3):378-385. (in Chinese)严洁,阮友田,薛珮瑶. 主被动光学图像融合技术研究[J]. 中国光学, 2015, 8(3):378-385.
[19]	Lv Yuanzhi, Sun Qiang, Bi Guoling. Recongnition and repairing of surface hole in three dimensional laser scanning system[J]. Chinese Optics, 2016, 9(1):114-121.. 吕源治, 孙强, 毕国玲. 三维激光扫描系统中曲面空洞的识别与修复[J]. 中国光学, 2016, 9(1):114-121.
[20]	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.

Proportional views

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

Get Citation

PDF

XML

Article Metrics

Article views(494) PDF downloads(40) Cited by()

Proportional views

LIDAR data segmentation method adapting to environmental characteristics

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. The Technology Center of Tianjin Zhonghuan Creative Technology Limited,Tianjin 300190,China

Received Date: 2018-03-10

Rev Recd Date: 2018-04-20

Publish Date: 2018-08-25

Key words:

Abstract: In order to solve the problem that LIDAR data segmentation algorithm cannot adapt to the environmental characteristics and determine the threshold continuously and accurately, an adaptive LIDAR data segmentation algorithm based on environmental features was proposed. According to the data characteristics of the two-dimensional lidar and the geometric characteristics of the indoor environment, the virtual environment line was fitted with the adjacent point of the laser radar data. The intersection of the virtual environment line and the adjacent laser scanning ray was taken as the reference point to determine the adaptive threshold pre-segmentation of radar data. In view of the defects in the data pre segmentation results completed by the above method, a method for judging pseudo breakpoints after data pre segmentation was proposed, and the algorithm was optimized. The algorithm was compared and analyzed with piecewise threshold segmentation algorithm and linear equation threshold segmentation algorithm. The LIDAR data segmentation algorithm adapting to environmental characteristics achieves a successful segmentation rate of 98% for the experimental data, and has better environment adaptability and higher segmentation accuracy.

HTML

Reference (20)

[1]	An S Y, Lee L K, Oh S Y. Ceiling vision-based active SLAM framework for dynamic and wide-open environments[J]. Autonomous Robots, 2016, 40(2):291-324.
[2]	Paneru B, Basnet N, Shrestha S, et al. Autonomous navigation of a mobile robot in indoor environments[J]. Zerone Scholar, 2016, 1(1):3-8.
[3]	Torres-Gonzlez A, Martnez-de Dios J R, Ollero A. Robot-beacon distributed range-only SLAM for resource-constrained operation[J]. Sensors, 2017, 17(4):903-921.
[4]	Koch P, May S, Schmidpeter M, et al. Multi-robot localization and mapping based on signed distance functions[J]. Journal of Intelligent Robotic Systems, 2016, 83(3-4):409-428.
[5]	Schaefer A, Luft L, Burgard W. An analytical lidar sensor model based on ray path information[J]. IEEE Robotics and Automation Letters, 2017, 2(3):1405-1412.
[6]	Gallant M J, Marshall J A. Two-dimensional axis mapping using LiDAR[J]. IEEE Transactions on Robotics, 2016, 32(1):150-160.
[7]	Misu K, Miura J. Specific person tracking using 3D LIDAR and ESPAR antenna for mobile service robots[J]. Advanced Robotics, 2015, 29(22):1483-1495.
[8]	Nguyen V, Gchter S, Martinelli A, et al. A comparison of line extraction algorithms using 2D range data for indoor mobile robotics[J]. Autonomous Robots, 2007, 23(2):97-111.
[9]	Borges G A, Aldon M J. Line extraction in 2D range images for mobile robotics[J]. Journal of Intelligent Robotic Systems, 2004, 40(3):267-297.
[10]	Lee M, Hur S, Park Y. Obstacle classification method using multi feature comparison based on single 2D LiDAR[J]. Journal of Institute of Control, Robotics and Systems, 2016, 22(4):253-265.
[11]	Wang W, Sakurada K, Kawaguchi N. Incremental and enhanced scanline-based segmentation method for surface reconstruction of sparse LiDAR data[J]. Remote Sensing, 2016, 8(11):967.
[12]	Li Junze. Research on indoor robot SLAM based on LIDAR[D]. Guangzhou:South China University of Technology,2016. (in Chinese)李昀泽. 基于激光雷达的室内机器人SLAM研究[D]. 广州:华南理工大学, 2016.
[13]	Hang Yijun, Liu Jianye, Li Rongbing, et al. MEMS IMU/LADAR integrated navigation method based on mixed feature match[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(9):2583-2592. (in Chinese)杭义军, 刘建业, 李荣冰, 等. 基于混合特征匹配的微惯性/激光雷达组合导航方法[J]. 航空学报, 2014, 35(9):2583-2592.
[14]	Cao Qiusheng. Technical discussion on imaging lidars as UAV payloads[J]. Infrared and Laser Engineering, 2016, 45(10):1003002. (in Chinese)曹秋生. 成像激光雷达的无人机载技术探讨[J]. 红外与激光工程, 2016, 45(10):1003002.
[15]	Shi Fengdong, Liu Wenhao, Wang Xin, et al. Design of indoor laser radar navigation system[J]. Infrared and Laser Engineering, 2015, 44(12):3570-3575. (in Chinese)史风栋, 刘文皓, 汪鑫, 等. 室内激光雷达导航系统设计[J]. 红外与激光工程, 2015, 44(12):3570-3575.
[16]	Liu Zhiqing, Li Pengcheng, Chen Xiaowei, et al. Classification of airborne LiDAR point cloud data based on information vector machine[J]. Optics and Precision Engineering, 2016, 24(1):210-219. (in Chinese)刘志青, 李鹏程, 陈小卫, 等. 基于信息向量机的机载激光雷达点云数据分类[J]. 光学精密工程, 2016, 24(1):210-219.
[17]	Yang Zijian, Chen Feng, Li Chao, et al. Transient effect of dead time of photon-counting in micro-pulse lidar[J]. Optics and Precision Engineering, 2015, 23(2):408-414. (in Chinese)杨子健, 陈锋, 李抄, 等. 微脉冲激光雷达中的光子计数死区时间瞬态效应[J]. 光学精密工程, 2015, 23(2):408-414.
[18]	Yan Jie, Ruan Youtian, Xue Peiyao. Active and passive optical image fusion technology[J]. Chinese Optics, 2015,8(3):378-385. (in Chinese)严洁,阮友田,薛珮瑶. 主被动光学图像融合技术研究[J]. 中国光学, 2015, 8(3):378-385.
[19]	Lv Yuanzhi, Sun Qiang, Bi Guoling. Recongnition and repairing of surface hole in three dimensional laser scanning system[J]. Chinese Optics, 2016, 9(1):114-121.. 吕源治, 孙强, 毕国玲. 三维激光扫描系统中曲面空洞的识别与修复[J]. 中国光学, 2016, 9(1):114-121.
[20]	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.

LIDAR data segmentation method adapting to environmental characteristics

doi: 10.3788/IRLA201847.0830001

Abstract

References

Proportional views

通讯作者: 陈斌, bchen63@163.com

Article Metrics

Related

Proportional views