Visual-Inertial-GNSS Tightly Coupled Navigation and Positioning Method with Fusion of Point and Line Features

doi:10.12068/j.issn.1005-3026.2025.20230292

Abstract

Abstract:

A multi-sensor fusion positioning method was proposed to address the limitations of single-sensor localization in complex environments. In terms of vision， line features were added to point features to overcome the interference caused by repetitive textures in visual images. In the global navigation satellite system （GNSS）， the introduction of carrier phase with higher accuracy was used to smooth the pseudorange observations， which improved the accuracy of single point positioning. The accuracy and stability of the algorithm were validated by using both public datasets and measured data. In both public datasets and actual data， the accuracy of the proposed method is improved by 32.2%， 23.3%， 24.5%， and 25.7%， 25.8%， and 14.1% in the X， Y， and Z directions， respectively， compared to the GVINS （visual inertial GNSS tightly coupled algorithm） in the geocentric coordinate system. In addition， in the environments where satellite signals are severely obstructed， the proposed method still has good positioning performance for a certain period of time， with a positioning accuracy of 0.74 m in plane and 0.91m in elevation. Research results provide new insights for multi-sensor fusion position in complex environments.

Key words: visual-inertial odometry, line feature, carrier phase smoothed pseudorange, graph optimization, tightly coupled

CLC Number:

TP 274

Li-ming HE, Quan-you YUE, Zheng-lin QU, Yu ZHANG. Visual-Inertial-GNSS Tightly Coupled Navigation and Positioning Method with Fusion of Point and Line Features[J]. Journal of Northeastern University(Natural Science), 2025, 46(4): 124-133.

Figures/Tables 14

Fig.1 Introduction to coordinate systems

Fig.2 Flowchart of the algorithm

Fig. 3 Triangulation of line feature

Fig. 4 Factor graph representation

Fig.5 Final trajectories from experiments on public datasets

Table 1 Experimental results on public datasets

算法	X轴	Y轴	Z轴
GVINS	0.901	0.516	0.485
本文方法	0.611	0.396	0.366

Fig. 6 ECEF error in public datasets

Fig.7 CDF of public dataset experiment errors

Fig.8 Comparison of the proposed algorithm with the GVINS algorithm under satellite degradation

Fig.9 Final trajectories of field experiments

Table 2 Field experimental results

算法	X方向	Y方向	Z方向
GVINS	0.901	0.669	0.581
本文方法	0.717	0.532	0.509

Fig.10 ECEF positioning error in field experiment

Fig. 11 CDF of field experimental error

Fig. 12 Comparison of positioning results between the proposed method and degraded RTK in the obstructed environment

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