Tight Integration of Feature-Based Relocalization in Monocular Direct Visual Odometry
MCML Authors
Mariia Gladkova
Daniel Cremers
Prof. Dr.
Director
Abstract
Mariia Gladkova
Daniel Cremers
Prof. Dr.
Director
Abstract
In this paper we propose a framework for inte-grating 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.
inproceedings GWZ+21
ICRA 2021
IEEE International Conference on Robotics and Automation. Xi'an, China, May 30-Jun 05, 2021.
Authors
M. Gladkova • R. Wang • N. Zeller • D. CremersLinks
DOIResearch Area
BibTeXKey: GWZ+21