An Algorithm for Road Boundary Extraction and Obstacle Detection Based on 3D Lidar
WANG Can1,2, KONG Bin1,2, YANG Jing1,2, WANG Zhiling2,3, ZHU Hui2,3
1.Special Robot Laboratory, Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031 2.Anhui Engineering Laboratory for Intelligent Driving Technology and Application, Hefei 230088 3.Research Center of Intelligent Vehicle Technology, Institute of Applied Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230088
Abstract To extract relevant road information quickly and effectively for intelligent vehicles in various road environments, an algorithm for real-time road boundary extraction and obstacle detection based on three-dimensional (3D) lidar is proposed. Firstly, 3D lidar point cloud data is rasterized and filtered, and the single beam laser point cloud spatial segmentation method is employed for spatial analysis to obtain the point cloud smoothness characteristic image. Then, the adaptive direction search algorithm is adopted to obtain the road boundary feature points and perform cluster analysis and curve fitting. Finally, the point cloud in the passable area is clustered and segmented under the road boundary constraint to obtain the obstacle position information. Experiments show that the proposed algorithm extracts road boundary and obstacle location information accurately in real time, and it meets the requirements of environment modeling and path planning for intelligent vehicle.
Fund:Supported by Key Research Program of National Natural Science Foundation of China(No.913203002), Technological Innovation Project for New Energy and Intelligent Networked Automobile Industry of Anhui Province, Innovation Research Project of Robotics and Intelligent Manufacturing, CAS(No.CO2018005), Science and Technology Planning Project of Guangdong Province(No.2016B090910002), Youth Spark Project of the Dean Found of Hefei Institute of Physical Science, CAS(No.YZJJ2020QN20)
Corresponding Authors:
WANG Can , master, assistant professor. Her research interests include pattern recognition and computer vision.
About author:: KONG Bin, Ph.D., professor. Her research interests include intelligent robot and machine vision.YANG Jing, Ph.D., associate professor. Her research interests include pattern recognition and computer vision. WANG Zhiling, Ph.D., professor. His re-search interests include driverless vehicle and machine vision.ZHU Hui, Ph.D., associate professor. His research interests include intelligent robot and multi-sensor information fusion.
[1] LEONARD J, HOW J, TELLER S, et al. A Perception-Driven Autonomous Urban Vehicle // BUEHLER M, IAGNEMMA K, SINGH S, eds. The DARPA Urban Challenge. Berlin, Germany: Springer, 2009: 163-230. [2] 陈 帅.无人驾驶汽车安全行驶的三大系统.中小企业管理与科技, 2018(10): 172-173. (CHEN S. Three Systems of Safe Driving of Driverless Vehicles. Management and Technology of SME, 2018(10): 172-173.) [3] 段建民,李帅印,王昶人,等.基于激光雷达的道路边界与障碍物检测研究.应用激光, 2018, 38(6): 1000-1007. (DUAN J M, LI S Y, WANG C R, et al. Detection of Road Boundaries and Obstacles Based on Lidar. Applied Laser, 2018, 38(6): 1000-1007.) [4] HATA A Y, OSORIO F S, WOLF D F. Robust Curb Detection and Vehicle Localization in Urban Environments // Proc of the IEEE Intelligent Vehicles Symposium. Washington, USA: IEEE, 2014: 1257-1262. [5] 蔡怀宇,陈延真,卓励然,等.基于优化DBSCAN算法的激光雷达障碍物检测.光电工程, 2019, 46(7): 180514-1-180514-8. (CAI H Y, CHEN Y Z, ZHUO L R, et al. LiDRA Object Detection Based on Optimized DBSCAN Algorithm. Opto-Electronic Engineering, 2019, 46(7): 180514-1-180514-8.) [6] GENG G Q, WU Z, JIANG H B, et al. Study on Path Planning Method for Imitating the Lane-Changing Operation of Excellent Dri-vers. Applied Sciences, 2018, 8(5): 814-820. [7] HAN J Y, KIM D C, LEE M, et al. Enhanced Road Boundary and Obstacle Detection Using a Downward-Looking Lidar Sensor. IEEE Transactions on Vehicular Technology, 2012, 61(3): 971-985. [8] 李 强,杜 煜.基于3D激光雷达道路边缘实时检测算法的研究与实现.计算机应用与软件, 2017, 34(10): 219-222. (LI Q, DU Y. Research and Implementation of Real-Time Road Edge Detection Algorithm Based on 3D Lidar. Computer Applications and Software, 2017, 34(10): 219-222.) [9] SINGH K, KAPOOR R, KR SINHA S. Enhancement of Low Exposure Images via Recursive Histogram Equalization Algorithms. Optik, 2015, 126(20): 2619-2625. [10] 刘 梓,唐振民,任明武.基于3D激光雷达的实时道路边界检测算法.华中科技大学学报(自然科学版), 2011, 39(增刊II): 351-354. (LIU Z, TANG Z M, REN M W. Algorithm of Real-Time Boundary Detection Based on 3D Lidar. Journal of Huazhong University of Science and Technology(Natural Science Edition), 2011, 39(Sup.II): 351-354.) [11] WANG H, GONG Y, HOU Y Y, et al. Road Detection Based on Image Boundary Prior // Proc of the International Conference on Image and Graphics. Berlin, Germany: Springer, 2015: 212-222. [12] 李毕祥,方 兰.激光雷达在复杂环境下车辆实时检测中的应用.激光杂志, 2017, 38(7): 192-195. (LI B X, FANG L. Laser Radar Application in Real-Time Vehicle Detection under Complicated Environment. Laser Journal, 2017, 38(7): 192-195.) [13] XU C, MI C, CHEN C, et al. Road Detection Based on Vanishing Point Location. Journal of Convergence Information Technology, 2012, 7(6): 137-145. [14] 王 灿,孔 斌,韦 虎.基于激光雷达机器人的目标人跟踪方法研究.计算机应用与软件, 2014, 31(7): 164-168. (WANG C, KONG B, WEI H. Research on Target Person Tracking Approach Based on Laser Radar Robot. Computer Applications and Software, 2014, 31(7): 164-168.) [15] ZAI D W, LI J, GUO Y L, et al. 3-D Road Boundary Extraction from Mobile Laser Scanning Data via Supervoxels and Graph Cuts. IEEE Transactions on Intelligent Transportation Systems, 2017, 19(3): 802-813. [16] SUN P P, ZHAO X M, XU Z G, et al. A 3D LiDAR Data-Based Dedicated Road Boundary Detection Algorithm for Autonomous Vehicles. IEEE Access, 2019, 7: 29623-29638. [17] RUSN R B, BLODOW N, MARTON Z C, et al. Close-Range Scene Segmentation and Reconstruction of 3D Point Cloud Maps for Mobile Manipulation in Domestic Environments // Proc of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Washington, USA: IEEE, 2009: 1-6. [18] HIMMELSBACH M, HUNDELSHAUSEN F V, WUENSCHE H J. Fast Segmentation of 3D Point Clouds for Ground Vehicles // Proc of the IEEE Intelligent Vehicles Symposium. Washington, USA: IEEE, 2010: 560-565. [19] DOUILLARD B, UNDERWOOD J, KUNTZ N, et al. On the Segmentation of 3D LIDAR Point Clouds // Proc of the IEEE International Conference on Robotics and Automation. Washington, USA: IEEE, 2011: 2798-2805. [20] 谢德胜,徐友春,王任栋,等.基于三维激光雷达的无人车障碍物检测与跟踪.汽车工程,2018, 40(8): 952-959. (XIE D S, XU Y C, WANG R D. Obstacle Detection and Tracking for Unmanned Vehicles Based on 3D Laser Radar. Automotive Engineering, 2018, 40(8): 952-959.) [21] HATA A Y, WOLF D F. Feature Detection for Vehicle Localization in Urban Environments Using a Multilayer LIDAR. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(2): 420-429. [22] LI K, SHAO J J, GUO D. A Multi-feature Search Window Method for Road Boundary Detection Based on LIDAR Data. Sensors, 2019, 19(7): 1551-1566. [23] HUANG R L, CHEN J J, LIU J, et al. A Practical Point Cloud Based Road Curb Detection Method for Autonomous Vehicle. Information, 2017, 8: 9-32. [24] 李广敬,鲍 泓,徐 成.一种基于3D激光雷达的实时道路边缘提取算法.计算机科学, 2018, 45(9): 294-298. (LI G J, BAO H, XU C. Real-Time Road Edge Extraction Algorithm Based on 3D-Lidar. Computer Science, 2018, 45(9): 294-298.) [25] 朱学葵,高美娟,李尚年.一种智能汽车的实时道路边缘检测算法.北京联合大学学报, 2015, 29(4): 1-7. (ZHU X Q, GAO M J, LI S N. A Real-Time Road Boundary Detection Algorithm Based on Driverless Cars. Journal of Beijing Union University, 2015, 29(4): 1-7.) [26] HU K L, WANG T M, LI Z, et al. Real-Time Extraction Method of Road Boundary Based on Three-Dimensional Lidar. Journal of Physics: Conference Series, 2018, 1074. DOI:10.1088/1742-6596/1074/D12080. [27] ELFES A. Using Occupancy Grids for Mobile Robot Perception and Navigation. Computer, 1989, 22(6): 46-57. [28] 王 灿.基于激光雷达的运动目标跟踪和地图构建技术研究.硕士学位论文.合肥:中国科学技术大学, 2013. (WANG C. Research on Moving Target Tracking and Map Buil-ding Technology Based on Laser Scanner. Master Dissertation. Hefei, China: University of Science and Technology of China, 2013.) [29] HOWARD A, SUKHATME G S, MATARIC M J. Multirobot Simultaneous Localization and Mapping Using Manifold Representations. Proceedings of the IEEE, 2006, 94(7): 1360-1369
2020, Vol. 33 
