Global-Map-Registered Local Visual Odometry Using On-the-Fly Pose Graph Updates

Masahiro Yamaguchi*, Shohei Mori, Hideo Saito, Shoji Yachida, Takashi Shibata

*Korrespondierende/r Autor/-in für diese Arbeit

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung


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 naïve VO.
TitelAugmented Reality, Virtual Reality, and Computer Graphics - 7th International Conference, AVR 2020, Proceedings
Redakteure/-innenLucio Tommaso De Paolis, Patrick Bourdot
Herausgeber (Verlag)Springer, Cham
ISBN (Print)9783030584641
PublikationsstatusVeröffentlicht - 7 Sep. 2020
Veranstaltung7th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics: AVR 2020 - Virtual, Lecce, Italien
Dauer: 7 Sep. 202010 Sep. 2020


NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band12242 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349


Konferenz7th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics
OrtVirtual, Lecce

ASJC Scopus subject areas

  • Theoretische Informatik
  • Informatik (insg.)

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