We propose a novel visual-inertial odometry approach that adopts structural regularity in man-made environments. Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity. An Atlanta world is a world that contains multiple local Manhattan worlds with different heading directions. Each local Manhattan world is detected on-the-fly, and their headings are gradually refined by the state estimator when new observations are coming. With fully exploration of structural lines that aligned with each local Manhattan worlds, our visual-inertial odometry method become more accurate and robust, as well as much more flexible to different kinds of complex man-made environments. Through extensive benchmark tests and real-world tests, the results show that the proposed approach outperforms existing visual-inertial systems in large-scale man-made environments
@article{arxiv.1810.06796,
title = {StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments},
author = {Danping Zou and Yuanxin Wu and Ling Pei and Haibin Ling and Wenxian Yu},
journal= {arXiv preprint arXiv:1810.06796},
year = {2019}
}