分布式多车协同视觉SLAM系统

doi:10.19562/j.chinasae.qcgc.2022.12.002

Abstract

Abstract:

Reliable localization and navigation are prerequisites for autonomous driving. Single-vehicle visual simultaneous localization and mapping（SLAM） enables vehicle localization in GNSS denial environment. However， the cumulative error will gradually increase with the running time， which leads to a great challenge of continuous and accurate localization. Therefore， localization can be improved by multi-vehicle collaborative visual SLAM. In this paper， a robust and lightweight distributed multi-vehicle collaborative visual SLAM system is proposed. The system uses ORB-SLAM2 as visual odometry， and uses global image descriptors NetVLAD for multi-vehicle place recognition and data association. A method based on data similarity and structural consistency is proposed to solve multi-vehicle loop-closure outlier rejection. Moreover， a distributed pose graph optimization method is proposed， which can enhance the accuracy of multi-vehicle collaborative localization. The system has been tested on our datasets collected by autonomous platform and KITTI datasets. The experiment results show that the proposed system outperforms the existing visual SLAM and collaborative SLAM.

Key words: multi-vehicle collaboration, SLAM, visual place recognition, autonomous vehicles

Chaoyang Jiang,Tianran Lan,Xiaoni Zheng,Jiulong Gao,Xuetong Ye. Distributed Multi-vehicle Collaborative Visual SLAM System[J].Automotive Engineering, 2022, 44(12): 1809-1817.

Figures/Tables 16

References 24

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算法步骤	最大值/m	平均值/m	RMSE/m
DGS	61.48	15.33	20.25
PCM+DGS	12.22	3.97	3.65
PCM+MC-DGS	11.55	3.18	2.95
本文系统	3.50	1.91	2.25

SLAM系统		RMSE/m
单车SLAM	VINS-Fusion	11.65
单车SLAM	ORB-SLAM2	6.82
协同SLAM	DSLAM	4.00
	DOOR-SLAM	8.00
	本文系统	2.25

数据集	轨迹长度/m	本文系统/m	DGS+PCM/m	ORB-SLAM2/m	VINS-Fusion/m
KITTI 00 城镇	3 724.187	2.25	3.65	6.82	11.65
KITTI 01 高速	2 453.203	5.19	5.74	10.30	6.78
KITTI 04 高速	393.645	0.28	0.28	0.17	0.19
KITTI 07 城镇	694.697	0.48	0.49	0.51	0.97
KITTI 08 城镇	3 222.795	1.47	1.40	3.58	9.89

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Distributed Multi-vehicle Collaborative Visual SLAM System

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