一种车载LiDAR点云道路提取深度神经网络模型

doi:10.13474/j.cnki.11-2246.2023.0351

测绘通报 ›› 2023, Vol. 0 ›› Issue (12): 8-12,18.doi: 10.13474/j.cnki.11-2246.2023.0351

• 道路与智能交通驾驶 • 上一篇

一种车载LiDAR点云道路提取深度神经网络模型

刘晋¹, 杨容浩¹, 文文¹, 谭骏祥¹, 兰青龙¹, 高祥¹, 汤洪²

1. 成都理工大学地球科学学院测绘工程系, 四川成都 610059;
2. 广州南方测绘科技股份有限公司成都分公司, 四川成都 610031

收稿日期:2023-06-01 发布日期:2024-01-08
通讯作者: 杨容浩。E-mail:yangronghao@cdut.edu.cn
作者简介:刘晋(1998-),男,硕士生,研究方向为点云语义分割、点云处理方法及应用。E-mail:ljlo1550830301@163.com
基金资助:
四川省科技计划(2021YJ0369)

A deep neural network model for road extraction of MLS LiDAR point cloud

LIU Jin¹, YANG Ronghao¹, WEN Wen¹, TAN Junxiang¹, LAN Qinglong¹, GAO Xiang¹, TANG Hong²

1. Department of Surveying & Mapping, College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China;
2. Chengdu Branch of South Surveying & Mapping Technology Co., Ltd., Chengdu 610031, China

Received:2023-06-01 Published:2024-01-08

摘要/Abstract

摘要： PointNet++在车载LiDAR点云道路提取中表现出优于传统方法的性能,但对于道路边缘的提取仍存在过分割或欠分割的现象。针对该问题,本文提出了一种改进的邻域增强编码网络——E-PointNet++,通过在特征提取前引入一个邻域增强编码模块,建立局部邻域内点与点之间的联系,以提高网络的道路边缘分割能力。在两个数据集上进行对比试验,E-PointNet++表现出明显优于其他方法的性能,准确性、完整性和检测质量均高于97%。该方法对于不同数据集和场景表现稳健。

关键词: 车载LiDAR点云, 深度学习, 道路提取, 边缘分割, 邻域增强编码

Abstract: PointNet++ has shown better performance than traditional methods in MLS LiDAR point cloud road extraction, but there are still the phenomena of over segmentation or under segmentation for road edge extraction.To address this issue, an improved neighborhood enhancement coding network E-PointNet++ is proposed. By introducing a neighborhood enhancement coding module before feature extraction, the connection between local neighborhood points is established to improve the network's road edge segmentation ability.Comparative experiments are conducted on two datasets, and E-PointNet++ shows significantly better performance than other methods, with accuracy, integrity and detection quality all exceeding 97%. This method performs robustly on different datasets and scenarios.

Key words: MLS LiDAR point cloud, deep learning, road extraction, edge segmentation, neighborhood enhanced coding

中图分类号:

P237

刘晋, 杨容浩, 文文, 谭骏祥, 兰青龙, 高祥, 汤洪. 一种车载LiDAR点云道路提取深度神经网络模型[J]. 测绘通报, 2023, 0(12): 8-12,18.

LIU Jin, YANG Ronghao, WEN Wen, TAN Junxiang, LAN Qinglong, GAO Xiang, TANG Hong. A deep neural network model for road extraction of MLS LiDAR point cloud[J]. Bulletin of Surveying and Mapping, 2023, 0(12): 8-12,18.

参考文献

[1] MA Lingfei,LI Ying,LI J,et al. Mobile laser scanned point-clouds for road object detection and extraction: a review[J]. Remote Sensing,2018,10(10): 1531.
[2] 王羽尘,于斌,陈晓阳,等. 基于激光雷达点云的道路几何信息提取与数字化建模研究[J]. 中国公路学报,2023,36(3):45-60.
[3] 惠振阳,胡友健,康妍斐. 基于反射强度偏度平衡的道路点云提取算法[J]. 激光与光电子学进展,2018,55(2):439-445.
[4] ZHANG Wuming,QI Jianbo,WAN Peng,et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J]. Remote Sensing,2016,8(6):501.
[5] 方莉娜,杨必胜. 车载激光扫描数据的结构化道路自动提取方法[J]. 测绘学报,2013,42(2):260-267.
[6] 胡啸,黄明,周海霞. 车载激光扫描数据的高速道路自动提取方法[J]. 测绘科学,2019,44(3):101-106.
[7] 卢健,贾旭瑞,周健,等. 基于深度学习的三维点云分割综述[J]. 控制与决策,2023,38(3):595-611.
[8] SU Hang,MAJI S,KALOGERAKIS E,et al. Multi-view convolutional neural networks for 3D shape recognition[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago:IEEE,2015:945-953.
[9] MENG H Y,GAO Lin,LAI Yukun,et al. VV-net:voxel VAE net with group convolutions for point cloud segmentation[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seou:IEEE,2019:8500-8508.
[10] MATURANA D,SCHERER S. VoxNet:a 3D Convolutional Neural Network for real-time object recognition[C]//Proceedings of 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).Hamburg:IEEE,922-928.
[11] BOULCH A,GUERRY J,LE SAUX B,et al. SnapNet:3D point cloud semantic labeling with 2D deep segmentation networks[J]. Computers & Graphics,2018,71:189-198.
[12] CHARLES R Q,HAO Su,MO Kaichun,et al. PointNet:deep learning on point sets for 3D classification and segmentation[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu:IEEE,2017:652-660.
[13] 杨晓文,王爱兵,韩燮,等. 基于KNN-PointNet的点云语义分割[J]. 激光与光电子学进展,2021,58(24):272-279.
[14] QI C R,YI Li,SU Hao,et al. PointNet++:deep hierarchical feature learning on point sets in a metric space[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. New York:ACM,2017:5105-5114.
[15] XU Mutian,DING Runyu,ZHAO Hengshuang,et al. PAConv:position adaptive convolution with dynamic kernel assembling on point clouds[C]// Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville:IEEE,2021:3173-3182.
[16] 焦晨,王宝锋,易耀华. 点云数据滤波算法研究[J]. 国外电子测量技术,2019,38(11):18-22.
[17] DESCHAUD J E,DUQUE D,RICHA J P,et al. Paris-CARLA-3D:a real and synthetic outdoor point cloud dataset for challenging tasks in 3D mapping[J]. Remote Sensing,2021,13(22):4713.
[18] 冉妮妮. 城市结构化道路路面点云提取方法研究[J]. 测绘技术装备,2022,24(1):57-61.
[19] 管郡智,潘卫清. 基于多区域RANSAC的地面点云提取算法[J]. 电子技术与软件工程,2020(14):176-177.

一种车载LiDAR点云道路提取深度神经网络模型

A deep neural network model for road extraction of MLS LiDAR point cloud

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