3D Modeling Method of Highway Based on Lidar Odometer

doi:10.12141/j.issn.1000-565X.220583

Abstract

Abstract:

The construction of 3D road digital model is of great significance for intelligent vehicle service and road management. In this paper, to solve the problems such as fast running speed, interference noise, few features and no loopback detection assistance existing in different sections of highway application scenarios, a three-dimension highway modeling method with lidar information as the modeling data base is proposed, in which multi-sensor fusion based on lidar odometry and LOAM technology is adopted. In the investigation, firstly, the point cloud data in different road scenarios are obtained by lidar, and the lidar image segmentation technique is used to assign each point a label about the structure and exclude the information of other moving vehicles on the road to reduce the modeling noise. Then, an accurate synchronization strategy is developed to integrate the sensors such as GNSS, IMU and lidar. On this basis, by combining the inertial navigation pre-integration results, the position constraint based on feature point cloud and the RTK data, a three-dimension highway digital model with global consistency is constructed to eliminate the cumulative error of the lidar odometry. Moreover, in order to maintain a finite number of attitude estimates, a sliding window optimization strategy based on key frames is introduced. Finally, three common road sections (general, bridge and tunnel) in the highway scenario are collected for modeling analysis, and the results show that the proposed approach can effectively improve the robustness, accuracy and validity in the challenging highway scenario modeling.

Key words: lidar odometry, highway, 3D modeling, factor graph optimization

CLC Number:

U495

HUANG Yan, FU Xinsha, ZENG Yanjie, et al. 3D Modeling Method of Highway Based on Lidar Odometer[J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(7): 129-138.

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传感器	定位模式	中断时间/s	位置精度/m		速度精度/（m·s^-1）
传感器	定位模式	中断时间/s	水平	垂直	水平	垂直
CGI-410	RTK	10	0.02	0.03	0.02	0.01
		30	0.65	0.20	0.06	0.02
		60	6.00	2.00	0.30	0.07

指标	LOAM方法结果	文中方法结果
ATE均方根误差/m	313.38	92.15
ATE最大值/m	579.62	146.56
ATE最小值/m	5.73	2.64
ATE中值/m	300.32	52.35
数据帧数	1 499	1 499
轨迹长度/m	3 812	3 812

指标	LOAM方法结果	文中方法结果
ATE均方根误差/m	221.59	21.17
ATE最大值/m	672.93	31.23
ATE最小值/m	6.19	2.09
ATE中值/m	108.53	21.72
数据帧数	551	551
轨迹长度/m	948	948

指标	LOAM方法结果	文中方法结果
ATE均方根误差/m	150.65	35.23
ATE最大值/m	456.45	60.64
ATE最小值/m	5.43	3.99
ATE中值/m	126.59	27.24
数据帧数	701	701
轨迹长度/m	1 538.5	1 538.5

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