Journal of Engineering Research
Journal of Engineering Research. 2024; 3: (3) ; 10.12208/j.jer.20240035 .
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中国中车股份有限公司 湖南株洲
*通讯作者： 刘良杰,单位：中国中车股份有限公司 湖南株洲；
为了更有效地获取可通行区域内的道路路面信息，提升三维激光雷达点云特征提取及点云分割的性能，本文提出了一种基于极坐标系的路面检测算法。与笛卡尔坐标系分割相比，利用极坐标对激光雷达产生的三维数据进行处理能够实现更加均匀且精确的分割效果，同时在分割点云时引入二维CNN可以大幅减小耗时。然后将得到的点云输入到PointNet进行栅格点云特征提取并引入RingCNN，最终得到了良好的路面检测准确率，为自动驾驶提供安全可靠的可行驶区域信息。
In order to more effectively obtain road surface information in the traversable area and improve the performance of three-dimensional laser radar point cloud feature extraction and point cloud segmentation, this paper proposes a road surface detection algorithm based on polar coordinates. Compared with Cartesian coordinate system segmentation, the use of polar coordinates to process the three-dimensional data generated by the laser radar can achieve a more uniform and accurate segmentation effect. At the same time, the introduction of two-dimensional CNN when segmenting the point cloud can greatly reduce the time consumption. Then the obtained point cloud is input into PointNet for grid point cloud feature extraction and RingCNN is introduced, and finally a good road surface detection accuracy is obtained, providing safe and reliable drivable area information for autonomous driving.
[1] Van, Brummelen, Jessica, et al. Autonomous vehicle perception: The technology of today and tomorrow[J]. Transportation Research Part C Emerging Technologies, 2018.
[2] Che E, Jung J, Olsen M. Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review[J]. Sensors, 2019, 19(4).
[3] Husain A, Vaishya R. A time efficient algorithm for ground point filtering from mobile lidar data. In Proceedings of the 2016 International Conference on Control, Computing, Communication and Materials (ICCCCM), Allahabad, India, 21–22 October 2016; pp. 1–5.
[4] Yadav M, Singh A. K, Lohani, B. Extraction of road surface from mobile lidar data of complex road environment. Int. J. Remote Sens. 2017, 38, 4655–4682.
[5] Wang G, Wu J, He R, et al. A point cloud-based robust road curb detection and tracking method[J]. IEEE Access, 2019, 7: 24611-24625.
[6] Kang Y. A lidar-Based Decision-Making Method for Road Boundary Detection Using Multiple Kalman Filters[J]. Industrial Electronics, IEEE Transactions on, 2012, 59(11):p.4360-4368.
[7] Cosgun A, Ma L, Chiu J, et al. Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California[C] 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017.
[8] Yadav M, Singh A K, Lohani B. Extraction of road surface from mobile lidar data of complex road environment[J]. International Journal of Remote Sensing, 2017, 38(16): 4655-4682.
[9] Guo Y, Wang H, Hu Q, et al. Deep learning for 3d point clouds: A survey[J]. arXiv preprint arXiv:1912.12033, 2019.
[10] Qi C R, Su H, Mo K, et al. Pointnet: Deep learning on point sets for 3d classification and segmentation[C]. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 652-660.
[11] Qi C R, Yi L, Su H, et al. Pointnet++: Deep hierarchical feature learning on point sets in a metric space[C]. Advances in neural information processing systems. 2017: 5099-5108.
[12] Li Y, Bu R, Sun M, et al. Pointcnn: Convolution on x-transformed points[C]. Advances in neural information processing systems. 2018: 820-830.
[13] Maturana D, Scherer S. Voxnet: A 3d convolutional neural network for real-time object recognition[C]. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015: 922-928.
[14] Liu Y, Fan B, Xiang S, et al. Relation-shape convolutional neural network for point cloud analysis[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 8895-8904.
[15] Ronneberger O, Fischer P, Brox T, U-Net: Convolutional Networks for Biomedical Image Segmentation[C] International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer International Publishing, 2015.
[16] Long J, Shelhamer E, Darrell T, Fully Convolutional Networks for Semantic Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4):640-651.
[17] Hu K, Wang T, Li Z, et al. Real-time extraction method of road boundary based on three-dimensional lidar[J]. J. Phys, 2018, 1074: 012080.
[18] Zhang Y, Wang J, Wang X, et al. 3d lidar-based intersection recognition and road boundary detection method for unmanned ground vehicle[C]. 2015 IEEE 18th International Conference on Intelligent Transportation Systems. IEEE, 2015: 499-504.
[19] Yadav M, Singh A K, Lohani B. Extraction of road surface from mobile lidar data of complex road environment[J]. International Journal of Remote Sensing, 2017, 38(16): 4655-4682.
[20] Chen Z, Zhang J, Tao D. Progressive lidar adaptation for road detection[J]. IEEE/CAA Journal of Automatica Sinica, 2019, 6(3): 693-702.
[21] Han X, Lu J, Zhao C, et al. Semisupervised and weakly supervised road detection based on generative adversarial networks[J]. IEEE Signal Processing Letters, 2018, 25(4): 551-555.
[22] Roynard X, JE Deschaud, Goulette F. Paris-Lille-3D: A Point Cloud Dataset for Urban Scene Segmentation and Classification[C] 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2018.
[23] Wang Y, Sun Y, Liu Z, et al. Dynamic Graph CNN for Learning on Point Clouds[J]. ACM Transactions on Graphics, 2018, 38(5).
[24] Zhang C, Luo W, Urtasun R, Efficient Convolutions for Real-Time Semantic Segmentation of 3D Point Clouds[C] 2018:399-408.
[25] Lang A H, Vora S, Caesar H, et al. PointPillars: Fast Encoders for Object Detection from Point Clouds[J]. 2018.
[26] Berman M, Triki A R, Blaschko M B, The Lovasz-Softmax Loss: A Tractable Surrogate for the Optimization of the Intersection-Over-Union Measure in Neural Networks[C] 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018.
[27] Lawin F J, Danelljan M, Tosteberg P, et al. Deep Projective 3D Semantic Segmentation[C] International Conference on Computer Analysis of Images and Patterns. Springer, Cham, 2017.
[28] Rethage D, Wald J, Sturm J, et al. Fully-Convolutional Point Networks for Large-Scale Point Clouds[J]. Springer, Cham, 2018.
[29] Thomas H, Qi C R, Deschaud J E, et al. KPConv: Flexible and Deformable Convolution for Point Clouds[C] 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2020.
[30] Liang Z, Yang M, Deng L, et al. Hierarchical Depthwise Graph Convolutional Neural Network for 3D Semantic Segmentation of Point Clouds[C] 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019.
[31] 张旻昊, 无人机激光雷达智能识别输电线路缺陷探究[J]. 工程学研究, 2022; 1: (5) : 77-80.
[32] 朱凯, 电动汽车技术革新与交通工程变革的深度融合[J]. 工程学研究, 2024; 3: (2) : 29-38.