多相机组合式全景视觉惯性SLAM系统

doi:10.12305/j.issn.1001-506X.2026.08.26

Abstract

Abstract:

To address the issues of decreased accuracy and robustness of visual localization algorithms in complex outdoor scenarios, as well as attitude drift caused by the rapid error accumulation of low-cost inertial measurement unit (IMU), a panoramic visual-inertial simultaneous localization and mapping (PVI-SLAM) algorithm is proposed. At the system architecture level, inertial states are deeply integrated into modules such as tracking, mapping, and loop closing to construct a visual-inertial fusion framework. Building on panoramic initialization, a parameter decoupling strategy and an analytical solution method are combined to calculate initial parameter values. It further employs maximum a posteriori estimation to complete inertial navigation initialization, thereby achieving accurate alignment between the visual and inertial state spaces. Meanwhile, the panoramic visual reprojection error and IMU pre-integration constraints are uniformly modeled as a nonlinear least squares problem, and the graph optimization method is used to complete state estimation. Experimental verification demonstrates that in complex outdoor scenarios, compared with existing mainstream SLAM solutions, the PVI-SLAM algorithm exhibits significant advantages in terms of state alignment performance, localization accuracy, and tracking robustness.

Key words: simultaneous localization and mapping (SLAM), multi-camera, panoramic visual-inertial alignment, state estimation

CLC Number:

TP 242

Desheng ZHUO, Feng ZHU, Xiaohong ZHANG, Junlong CHENG, Jie HU. Multi-camera combined panoramic visual-inertial SLAM system[J]. Systems Engineering and Electronics, 2026, 48(8): 2809-2820.

Figures/Tables 15

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References 31

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序列	PAN-SLAM		本文
序列	追踪数量	中断	追踪数量	中断
00	234	3	302	0
01	254	2	328	0

序列	PAN-SLAM		本文算法
序列	RMSE	STD	RMSE	STD
00	4.47	2.88	3.34	1.46
01	2.74	1.75	1.37	0.78

序列	PAN-ORBSLAM3			本文算法
序列	尺度误差/%	RMSE/m	STD/m	尺度误差/%	RMSE/m	STD/m
02	20.15	0.0166	0.0112	19.96	0.0085	0.0051
03	24.47	0.0062	0.0039	5.53	0.0046	0.0026
04	36.80	0.0045	0.0021	11.73	0.0039	0.0016
05	31.13	0.0032	0.0020	3.53	0.0020	0.0012
均值	28.14	0.0076	0.0048	10.19	0.0048	0.0026

算法	融合模式	相机数目	惯导数目
VINS-Mono	单目惯性	1	1
ORB-SLAM3	单目惯性	1	1
OpenVINS	单目惯性	1	1
OpenVINS（multi）	多组单目惯性	4	1
本文算法	全景惯性	4	1

序列	VINS-Mono		ORB-SLAM3		OpenVINS		OpenVINS（multi）		本文算法
序列	RMSE	STD	RMSE	STD	RMSE	STD	RMSE	STD	RMSE	STD
02	17.90	9.39	—	—	28.29	8.43	11.09	7.20	0.48	0.17
03	10.73	4.92	30.61	12.94	12.44	6.11	11.18	4.95	2.93	1.63
04	22.87	9.47	—	—	8.05	3.19	6.74	3.58	1.84	0.81
05	10.64	6.19	15.53	6.76	16.51	5.94	6.09	3.27	2.99	1.31

Multi-camera combined panoramic visual-inertial SLAM system

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