基于稀疏彩色点云的自动驾驶汽车3D目标检测方法

doi:10.19562/j.chinasae.qcgc.2021.04.006

Abstract

Abstract:

Aiming at the current problem of the low accuracy of point cloud segmentation and recognition algorithm in object detection of autonomous vehicle， a sparse color point cloud （SCPC） structure is proposed， which is formed by spatial matching and feature superposition of the image information collected by camera and the point cloud information acquired from lidar. Then， the improved PointPillars neural network algorithm is adopted to conduct operation on the fused SCPC. The results of experiment show that this method can achieve a major rise in average accuracy， compared with original PointPillars algorithm， especially the recognition accuracy of pedestrians and cyclists. The average accuracy of pedestrian and cyclist detections on 3D view under medium difficulty increases by 13.8% and 6.6% respectively， demonstrating the effectiveness of the method adopted.

Key words: lidar, sensor fusion, neural network, object detection

Yutao Luo,Han Qin. 3D Object Detection Method for Autonomous Vehicle Based on Sparse Color Point Cloud[J].Automotive Engineering, 2021, 43(4): 492-500.

Figures/Tables 15

References 28

1	HIMMELSBACH M， MUELLER A， LÜTTEL T， et al. LIDAR⁃based 3D object perception［C］. Proceedings of 1st International Workshop on Cognition for Technical Systems，2008.
2	CHARLES R Q， SU H， KAICHUN M， et al. PointNet： deep learning on point sets for 3D classification and segmentation［C］. 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR），IEEE Computer Society， 2017.
3	YANG B， LIANG M， URTASUN R. Hdnet： Exploiting HD maps for 3D object detection［C］. Conference on Robot Learning，PMLR， 2018.
4	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， IEEE， 2018.
5	YAN Y， MAO Y， LI B. Second： sparsely embedded convolutional detection［J］. Sensors， 2018，18（10）： 3337.
6	LANG A H， VORA S， CAESAR H， et al. PointPillars： fast encoders for object detection from point clouds［C］. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）， 2019.
7	CHEN X， MA H， WAN J， et al. Multi⁃view 3D object detection network for autonomous driving［C］. 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）， IEEE， 2017.
8	LIANG M， YANG B， WANG S， et al. Deep continuous fusion for multi⁃sensor 3D object detection［C］. European Conference on Computer Vision， Springer， 2018.
9	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）， IEEE， 2018.
10	CHO H， SEO Y， KUMAR B V， et al. A multi⁃sensor fusion system for moving object detection and tracking in urban driving environments［C］. 2014 IEEE International Conference on Robotics and Automation （ICRA），IEEE， 2014.
11	CHAVEZ-GARCIA R O， AYCARD O. Multiple sensor fusion and classification for moving object detection and tracking［J］. IEEE Transactions on Intelligent Transportation Systems， 2015，17（2）： 525-534.
12	KU J， PON A D， WALSH S， et al. Improving 3D object detection for pedestrians with virtual multi⁃view synthesis orientation estimation［C］. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）， IEEE， 2019.
13	KU J， PON A D， WASLANDER S L. Monocular 3D object detection leveraging accurate proposals and shape reconstruction［C］. IEEE Computer Society， 2019.
14	VORA S， LANG A H， HELOU B， et al. PointPainting： sequential fusion for 3D object detection［C］. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）， 2020.
15	FISHER B. "3x4Projection Matrix" from geometric framework for vision I ［EB/OL］. （1997-04-16）［2021-03-02］. .
16	FISHER B. "Camera Calibration"from geometric framework for vision I［EB/OL］. （1997-04-16）［2021-03-02］. .
17	GEIGER A， LENZ P， STILLER C， et al. Vision meets robotics： the KITTI dataset［J］. The International Journal of Robotics Research， 2013，32（11）： 1231-1237.
18	LIU W， ANGUELOV D， ERHAN D， et al. Ssd： single shot multibox detector［C］. European Conference on Computer Vision， Springer， 2016.
19	IOFFE S， SZEGEDY C. Batch normalization： accelerating deep network training by reducing internal covariate shift［C］. International Conference on Machine Learning， 2015. PMLR.
20	NAIR V， HINTON G E. Rectified linear units improve restricted Boltzmann machines［C］. Proceedings of the 27th International Conference on International Conference on Machine Learning， 2010.
21	EVERINGHAM M， Van GOOL L， WILLIAMS C K， et al. The pascal visual object classes （VOC） challenge［J］. International Journal of Computer Vision， 2010，88（2）： 303-338.
22	LIN T， GOYAL P， GIRSHICK R B， et al. Focal loss for dense object detection［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2018，2（42）： 318-327.
23	CHEN X， KUNDU P K， ZHU Y， et al. 3D object proposals for accurate object class detection［C］. Advances in Neural Information Processing Systems， 2015： 424-432.
24	GEIGER A， LENZ P， URTASUN R. Are we ready for autonomous driving？ the KITTI vision benchmark suite［C］. 2012 IEEE Conference on Computer Vision and Pattern Recognition， IEEE， 2012.
25	YANG Z， SUN Y， LIU S， et al. STD： sparse⁃to⁃dense 3D object detector for point cloud［C］. 2019 IEEE/CVF International Conference on Computer Vision （ICCV）， IEEE， 2019.
26	YANG Z， SUN Y， LIU S， et al. IPOD： intensive point⁃based object detector for point cloud［J］. arXiv preprint， 2018： 1812.
27	QI C R， LIU W， WU C， et al. Frustum PointNets for 3D object detection from RGB-D data［C］. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition， 2018.
28	SHIN K， KWON Y P， TOMIZUKA M. RoarNet： a robust 3D object detection based on region approximation refinement［C］. 2019 IEEE Intelligent Vehicles Symposium （IV）， IEEE， 2019.

[1]	WANG Dan, LIU Zhong-Chang, TIAN Jing, HAN Yong-Qiang, TAN Man-Zhi. [J]. , 2016, 38(12): 1415 -1419 .
[2]	KONG Xiang-Dong, ZHANG Zhen-Dong, XIE Nai-Liu, CHENG Qiang, YIN Cong-Bo. [J]. , 2016, 38(12): 1471 -1476 .
[3]	LI Ling, MA Li, MOU Yu, XU Chao, LI Wen-Ru, SHI Shu-Ming. [J]. , 2016, 38(12): 1508 -1514 .
[4]	GAO Ji-Dong, GAO Bo-Lin, XIE Shu-Gang. [J]. , 2016, 38(12): 1515 -1520 .
[5]	QIN Xiao-Hui, WANG Jian-Qiang, XIE Bo-Yuan, HU Man-Jiang, LI Ke-Qiang. [J]. , 2017, 39(1): 73 -78 .
[6]	SU Chang, FU Li-Ming, WEI Jun, CAO Yue, HUANG Lei, LI Shuo. [J]. , 2017, 39(1): 35 -40 .
[7]	ZHANG Jian, ZHOU Dao-Kui, TONG Rui, QI Hao-Chen. [J]. , 2017, 39(1): 47 -51 .
[8]	ZHENG Wei. [J]. , 2017, 39(1): 107 -112 .
[9]	SHAO Shi-Ping, HUANG Ju-Hua, CAO Ming. [J]. , 2017, 39(2): 200 -205 .
[10]	Chen-Xin, FENG Xiao, SHEN Chuan-Liang, WANG Shuo, HU Cui-Song, LI Yan-Yang, YANG Chang-Hai-　. [J]. , 2017, 39(2): 206 -213 .

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3D Object Detection Method for Autonomous Vehicle Based on Sparse Color Point Cloud

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