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

• URL: http://arxiv.org/abs/2102.01191v1
• Date: Mon, 1 Feb 2021 21:41:05 GMT
• Title: Tight-Integration of Feature-Based Relocalization in Monocular Direct Visual Odometry
• Authors: Mariia Gladkova, Rui Wang, Niclas Zeller, and Daniel Cremers
• Abstract summary: We propose a framework for integrating map-based relocalization into online visual odometry. We integrate image features into Direct Sparse Odometry (DSO) and rely on feature matching to associate online visual odometry with a previously built map.
• Score: 49.89611704653707
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 treated as pose priors and tightly integrated into the direct image alignment of the front-end tracking. Secondly, they are also tightly integrated into the back-end bundle adjustment. 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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