- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | WANG Tao, ZENG Wenhao, YU Qi. Obstacle Detection and Tracking for Driverless Cars Based on Lidar[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1346-1354. doi: 10.3969/j.issn.0258-2724.20200240
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To improve clustering adaptability, real-time performance, and tracking accuracy in the process of dynamic obstacle detection and tracking with lidar, an adaptive density clustering algorithm and a multi-feature data association method are developed for detection and tracking respectively. Firstly, the point cloud collected by the lidar is pre-processed such as curb detection, area-of-interest extraction and ground segmentation to remove irrelevant point clouds. Then the non-ground point clouds are clustered according to the adaptive clustering algorithm to complete the obstacle point cloud detection. Finally, a weighted multi-feature data association algorithm combined with Kalman filter is used to track dynamic obstacles. The experiments show that the entire detection process with the proposed methods can accurately and stably detect and track the 10 Hz lidar data, and shorten the clustering time by 32%.
| [1] |
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[J]. 汽车工程,2018,40(8): 952-959.
XIE Desheng, XU Youchun, WANG Rendong, et al. Obstacle detection and tracking for unmanned vehicles based on 3D laser radar[J]. Automotive Engineering, 2018, 40(8): 952-959.
|
| [2] |
AZIM A, AYCARD O. Layer-based supervised classification of moving objects in outdoor dynamic environment using 3D laser scanner[C]//2014 IEEE Intelligent Vehicles Symposium Proceedings. Dearborn: IEEE, 2014: 1408-1414.
|
| [3] |
黄钢,吴超仲,吕能超. 基于改进DBSCAN算法的激光雷达目标物检测方法[J]. 交通信息与安全,2015,33(3): 23-28. doi: 10.3963/j.issn.1674-4861.2015.03.004
HUANG Gang, WU Chaozhong, LYU Nengchao. A study of laser radar object detection based on improved DBSCAN algorithm[J]. Journal of Transport Information and Safety, 2015, 33(3): 23-28. doi: 10.3963/j.issn.1674-4861.2015.03.004
|
| [4] |
包瑞胜,马新,崔熠明. 基于优化DBSCAN算法的智能车载激光雷达数据处理技术研究[J]. 中国交通信息化,2017(3): 135-140.
|
| [5] |
ZHANG J S, KEREKES J. An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(4): 726-730. doi: 10.1109/LGRS.2014.2360367
|
| [6] |
CHEN J R, XU H, WU J Q, et al. Deer crossing road detection with roadside LiDAR sensor[J]. IEEE Access, 2019, 7: 65944-65954. doi: 10.1109/ACCESS.2019.2916718
|
| [7] |
LEONARD J, HOW J, TELLER S, et al. A perception-driven autonomous urban vehicle[J]. Journal of Field Robotics, 2008, 25(10): 727-774. doi: 10.1002/rob.20262
|
| [8] |
NAVARRO-SERMENT L E, MERTZ C, HEBERT M. Pedestrian detection and tracking using three-dimensional LiDAR data[J]. The International Journal of Robotics Research, 2010, 29(12): 1516-1528. doi: 10.1177/0278364910370216
|
| [9] |
汪世财,谈东奎,谢有浩,等. 基于激光雷达点云密度特征的智能车障碍物检测与跟踪[J]. 合肥工业大学学报(自然科学版),2019,42(10): 1311-1317. doi: 10.3969/j.issn.1003-5060.2019.10.003
WANG Shicai, TAN Dongkui, XIE Youhao, et al. Obstacle detection and tracking for intelligent vehicle based on density characteristics of point cloud using 3D lidar[J]. Journal of Hefei University of Technology(Natural Science), 2019, 42(10): 1311-1317. doi: 10.3969/j.issn.1003-5060.2019.10.003
|
| [10] |
HAN J, KIM D, LEE M, et al. Enhanced road boundary and obstacle detection using a downward-looking LIDAR sensor[J]. IEEE Transactions on Vehicular Technology, 2012, 61(3): 971-985. doi: 10.1109/TVT.2012.2182785
|
| [11] |
ZHOU Y, WANG D, XIE X, et al. A fast and accurate segmentation method for ordered LiDAR point cloud of large-scale scenes[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(11): 1981-1985. doi: 10.1109/LGRS.2014.2316009
|
| [12] |
XU S, WANG R S, WANG H, et al. An optimal hierarchical clustering approach to mobile LiDAR point clouds[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(7): 2765-2776. doi: 10.1109/TITS.2019.2912455
|
| [13] |
刘健. 基于三维激光雷达的无人驾驶车辆环境建模关键技术研究[D]. 合肥: 中国科学技术大学, 2016.
|
| [14] |
ZHANG Y S, SUN X, XU H, et al. Tracking multi-vehicles with reference points switches at the intersection using a roadside LiDAR sensor[J]. IEEE Access, 2019, 7: 174072-174082. doi: 10.1109/ACCESS.2019.2953747
|
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