TY - GEN
T1 - Global-map-registered local visual odometry using on-the-fly pose graph updates
AU - Yamaguchi, Masahiro
AU - Mori, Shohei
AU - Saito, Hideo
AU - Yachida, Shoji
AU - Shibata, Takashi
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Graph optimization
KW - Location-based AR
KW - Structure from motion
KW - Visual Odometry
UR - http://www.scopus.com/inward/record.url?scp=85091188204&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85091188204&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-58465-8_23
DO - 10.1007/978-3-030-58465-8_23
M3 - Conference contribution
AN - SCOPUS:85091188204
SN - 9783030584641
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 299
EP - 311
BT - Augmented Reality, Virtual Reality, and Computer Graphics - 7th International Conference, AVR 2020, Proceedings
A2 - De Paolis, Lucio Tommaso
A2 - Bourdot, Patrick
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on Augmented Reality, Virtual Reality, and Computer Graphics, AVR 2020
Y2 - 7 September 2020 through 10 September 2020
ER -