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arXiv · 2102.01191

Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry

Mariia Gladkova·Rui Wang·Niclas Zeller·Daniel Cremers
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Abstract

In this paper we propose a framework for integrating map-based relocalization into online direct visual odometry. To achieve map-based relocalization for direct methods, we integrate image features into Direct Sparse Odometry (DSO) and rely on feature matching to associate online visual odometry (VO) with a previously built map. The integration of the relocalization poses is threefold. Firstly, they are incorporated as pose priors in the direct image alignment of the front-end tracking. Secondly, they are tightly integrated into the back-end bundle adjustment. Thirdly, an online fusion module is further proposed to combine relative VO poses and global relocalization poses in a pose graph to estimate keyframe-wise smooth and globally accurate poses. We evaluate our method on two multi-weather datasets showing the benefits of integrating different handcrafted and learned features and demonstrating promising improvements on camera tracking accuracy.

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Mariia Gladkova, Rui Wang, Niclas Zeller, Daniel Cremers. 2021-02-01. Tight Integration of Feature-based Relocalization in Monocular Direct Visual Odometry. https://arxiv.org/abs/2102.01191

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