一种高动态场景下视觉激光融合SLAM系统

doi:10.19562/j.chinasae.qcgc.2025.09.007

Abstract

Abstract:

Accurate localization and mapping are critical for autonomous driving systems. However， single-sensor Simultaneous Localization and Mapping （SLAM） systems often struggle to operate reliably across different environment， particularly in highly dynamic scenes where dynamic obstacles can degrade accuracy or even cause system failure. Therefore， in this paper a LiDAR-Visual fusion SLAM framework tailored for high-precision mapping and localization problems in dynamic environment is proposed. Firstly， an odometry method that fuses sparse LiDAR point clouds with dense image data is designed， leveraging the high-precision ranging capabilities of LiDAR and the rich information provided by images to enhance odometry accuracy. To address challenges in highly dynamic scenes， based on a real-time image semantic segmentation network， BiSeNetV2， combined with motion feature detection techniques based on inter-frame and multi-frame sequences， the efficient and accurate identification of dynamic points among the 3D feature points obtained from the LiDAR-Visual fusion is realized， which are removed from the map to mitigate the influence of dynamic obstacles. Tests are carried out on the nuScenes autonomous driving dataset， and the results show significant improvement of the proposed system in accuracy and robustness of localization and mapping in dynamic environment.

Key words: SLAM, image, LiDAR, multi-sensor fusion, high-dynamic environment

Yunshui Zhou,Chengyu Gao,Shengjie Huang,Runbang Zhang,Xin Chen,Yougang Bian,Hongmao Qin. LiDAR-Visual Fusion SLAM System for High-Dynamic Environment[J].Automotive Engineering, 2025, 47(9): 1712-1720.

Figures/Tables 9

References 26

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数据集	480×640	1280×720	精度下降率
Cityscape	7.38 ms	12.59 ms	0.67 %
ADE20K	7.74 ms	15.03 ms	2.89 %

序列	本文里程计			激光语义过滤方法			本文方法
序列	RMSE/m	Mean/m	STD	RMSE/m	Mean/m	STD	RMSE/m	Mean/m	STD
103	1.677	1.373	0.038	0.851	0.785	0.227	0.537	0.473	0.093
553	1.191	0.894	0.029	0.015	0.014	0.002	0.073	0.016	0.079
757	1.354	1.371	0.138	0.316	0.308	0.069	0.711	0.201	1.337
1 077	2.428	2.031	1.751	3.482	2.499	2.425	1.871	0.968	1.114
1 094	1.876	1.621	0.025	1.476	0.966	1.117	1.124	1.109	0.111
1 100	0.925	0.725	0.033	0.081	0.067	0.045	0.073	0.063	0.017

序列	RMSE/m
序列	ORBSlam3	VLOAM	本文里程计	里程计+语义
103	2.815	0.521	1.677	0.537
553	1.261	0.217	1.191	0.073
757	2.775	2.376	1.354	0.711
1 077	2.643	2.438	2.428	1.871
1 094	1.928	1.531	1.876	1.124
1 100	1.072	0.331	0.925	0.073

LiDAR-Visual Fusion SLAM System for High-Dynamic Environment

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