A deep monocular visual-inertial navigation algorithm with SuperGlue

doi:10.13474/j.cnki.11-2246.2024.0220

Abstract

Abstract: The deep learning method for images is an effective way to solve the problems of unstable feature extraction and tracking loss of traditional visual positioning algorithms in complex environments. In this paper, we propose a visual-inertial navigation algorithm based on VINS-Mono, which using SuperPoint to get feature points and track them by using SuperGlue. And evaluate it using Open-source dataset and real world experiments. Experimental results show that our algorithm has a significant improvement in positioning accuracy and stability compared with VINS-Mono, and the accuracy improvement can reach 26%.

Key words: visual-inertial navigation, deep learning, SuperPoint, SuperGlue, VINS-Mono

CLC Number:

P228
P237

LIU Yibo, WU Chuanwen, ZHOU Zongkun, CHEN Hua. A deep monocular visual-inertial navigation algorithm with SuperGlue[J]. Bulletin of Surveying and Mapping, 2024, 0(2): 113-117.

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