@inproceedings{a176cbe7a0ad4aa2b4a61b9394907992,
title = "Global-Map-Registered Local Visual Odometry Using On-the-Fly Pose Graph Updates",
abstract = "Real-time camera pose estimation is one of the indispensable technologies for Augmented Reality (AR). While a large body of work in Visual Odometry (VO) has been proposed for AR, practical challenges such as scale ambiguities and accumulative errors still remain especially when we apply VO to large-scale scenes due to limited hardware and resources. We propose a camera pose registration method, where a local VO is consecutively optimized with respect to a large-scale scene map on the fly. This framework enables the scale estimation between a VO map and a scene map and reduces accumulative errors by finding corresponding locations in the map to the current frame and by on-the-fly pose graph optimization. The results using public datasets demonstrated that our approach reduces the accumulative errors of na{\"i}ve VO.",
keywords = "Graph optimization, Location-based AR, Structure from motion, Visual Odometry",
author = "Masahiro Yamaguchi and Shohei Mori and Hideo Saito and Shoji Yachida and Takashi Shibata",
year = "2020",
month = sep,
day = "7",
doi = "10.1007/978-3-030-58465-8_23",
language = "English",
isbn = "9783030584641",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer, Cham",
pages = "299--311",
editor = "{De Paolis}, {Lucio Tommaso} and Patrick Bourdot",
booktitle = "Augmented Reality, Virtual Reality, and Computer Graphics - 7th International Conference, AVR 2020, Proceedings",
note = "7th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics : AVR 2020 ; Conference date: 07-09-2020 Through 10-09-2020",
}