Real-Time Detection of Small Obstacles Based on 16-Ray Lidar Point Cloud

Ying Han; Jing Yuan; Jiangsheng Si; Dehe Yang

doi:10.3788/LOP202158.1228001

[1] Hu Y W, Wang J J, Fan Y Y et al. LiDAR-based three-dimensional modeling and volume calculation for space objects[J]. Chinese Journal of Lasers, 47, 0510001(2020).

[2] Ojeda R G, Briales J, Jimenez J G et al. PL-SVO: semi-direct monocular visual odometry by combining points and line segments[C]. //2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 9-14, 2016, Daejeon, Korea (South), 4211-4216(2016).

[3] Oh Y J, Watanabe Y. Development of small robot for home floor cleaning[C]. //Proceedings of the 41st SICE Annual Conference SICE 2002, August 5-7, 2002, Osaka, Japan., 3222-3223(2002).

[4] Park Y, Lepetit V, Woo W et al. Multiple 3D object tracking for augmented reality[C]. //2008 7th IEEE/ACM International Symposium on Mixed and Augmented Reality, September 15-18, 2008, Cambridge, UK., 117-120(2008).

[5] Zhao M N, Hua X H, Feng S Q et al. Information extraction of buildings, doors, and windows based on point cloud slices[J]. Chinese Journal of Lasers, 47, 0604002(2020).

[6] Chen X Z, Ma H M, Wan J et al. Multi-view 3D object detection network for autonomous driving[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 6526-6534(2017).

[7] Geiger A, Lenz P, Urtasun R et al. Are we ready for autonomous driving? The KITTI vision benchmark suite[C]. //2012 IEEE Conference on Computer Vision and Pattern Recognition, June 16-21, 2012, Providence, RI, USA., 3354-3361(2012).

[8] Cao H W, Yang L H, Lin J R et al. Networking method of distributed measurement network fused with autonomous navigation information[J]. Laser & Optoelectronics Progress, 57, 151201(2020).

[9] Zhou Y, Tuzel O. VoxelNet: end-to-end learning for point cloud based 3D object detection[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 4490-4499(2018).

[10] Engelcke M, Rao D, Wang D Z et al. Vote3Deep: fast object detection in 3D point clouds using efficient convolutional neural networks[C]. //2017 IEEE International Conference on Robotics and Automation (ICRA), May 29-June 3, 2017, Singapore, 1355-1361(2017).

[11] Qi C R, Liu W, Wu C X et al. Frustum PointNets for 3D object detection from RGB-D data[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 918-927(2018).

[12] Wang S K, Sun Y X, Liu C J et al. PointTrackNet: an end-to-end network for 3-D object detection and tracking from point clouds[J]. IEEE Robotics and Automation Letters, 5, 3206-3212(2020).

[13] Ku J, Mozifian M, Lee J et al. Joint 3D proposal generation and object detection from view aggregation[C]. //2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), October 1-5, 2018, Madrid, Spain., 1-8(2018).

[14] Li B, Zhang T L, Xia T et al. Vehicle detection from 3D lidar using fully convolutional network[C]. //Robotics: Science and Systems XII, August 29, 2016, AnnArbor, Michigan, 1608-1616(2016).

[15] Li B. 3D fully convolutional network for vehicle detection in point cloud[C]. //2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 24-28, 2017, Vancouver, BC, Canada., 1513-1518(2017).

[16] Yu Z J, He X. Research on intrusion clearance detection system for high-speed railway based on binocular stereo vision[C]. //Proceedings of 2011 IEEE International Conference on Service Operations, Logistics and Informatics, July 10-12, 2011, Beijing, China., 532-536(2011).

[17] Ning B, Yu Z J, Zhu L Q et al. Remote observation system of railway and its application[J]. Journal of the China Railway Society, 36, 62-69(2014).

[18] Zhang J L. Automatic track montitoring and alarming system and its application[J]. China Railway, 2019, 95-99.

[19] Liu Y. Research on improved intrusion monitoring scheme for railways[J]. Railway Signalling & Communication Engineering, 10, 30-32(2013).

[20] Shi H M, Chai H, Wang Y et al. Study on railway embedded detection algorithm for railway intrusion based on object recognition and tracking[J]. Journal of the China Railway Society, 37, 58-65(2015).

[21] Dong H H, Ge D W, Qin Y et al. Research on railway invasion detection technology based on intelligent video analysis[J]. China Railway Science, 31, 121-125(2010).

[22] Zheng Y H, He J Y. Research on obstacle monitoring and alarm system on railway lines[J]. China Railway, 111-117(2019).

[23] Fan X H, Xu G L, Li W L et al. Target segmentation method for three-dimensional LiDAR point cloud based on depth image[J]. Chinese Journal of Lasers, 46, 0710002(2019).

[24] Su Y T, Bethel J, Hu S W et al. Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 113, 59-74(2016).

[25] Vo A V, Hong L T, Laefer D F et al. Octree-based region growing for point cloud segmentation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 88-100(2015).

[26] Han X F, Jin J S, Wang M J et al. A review of algorithms for filtering the 3D pointcloud[J]. Signal Processing: Image Communication, 57, 103-112(2017).

[27] Schall O, Belyaev A, Seidel H P et al. Adaptive feature-preserving non-local denoising of static and time-varying range data[J]. Computer-Aided Design, 40, 701-707(2008).