Distributed Photometric Bundle Adjustment
Demmel N, Gao M, Laude E, Wu T, Cremers D (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Pages Range: 140-149
Conference Proceedings Title: Proceedings - 2020 International Conference on 3D Vision, 3DV 2020
Event location: Virtual, Fukuoka, JPN
ISBN: 9781728181288
DOI: 10.1109/3DV50981.2020.00024
Abstract
In this paper we demonstrate that global photometric bundle adjustment (PBA) over all past keyframes can significantly improve the global accuracy of a monocular SLAM map compared to geometric techniques such as pose-graph optimization or traditional (geometric) bundle adjustment. However, PBA is computationally expensive in runtime, and memory usage can be prohibitively high. In order to address this scalability issue, we formulate PBA as an approximate consensus program. Due to its decomposable structure, the problem can be solved with block coordinate descent in parallel across multiple independent workers, each having lower requirements on memory and computational resources. For improved accuracy and convergence, we propose a novel gauge aware consensus update. Our experiments on real-world data show an average error reduction of 62% compared to odometry and 33% compared to intermediate pose-graph optimization, and that compared to the central optimization on a single machine, our distributed PBA achieves competitive pose-accuracy and cost.
Involved external institutions
Germany (DE)How to cite
APA:
Demmel, N., Gao, M., Laude, E., Wu, T., & Cremers, D. (2020). Distributed Photometric Bundle Adjustment. In Proceedings - 2020 International Conference on 3D Vision, 3DV 2020 (pp. 140-149). Virtual, Fukuoka, JPN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Demmel, Nikolaus, et al. "Distributed Photometric Bundle Adjustment." Proceedings of the 8th International Conference on 3D Vision, 3DV 2020, Virtual, Fukuoka, JPN Institute of Electrical and Electronics Engineers Inc., 2020. 140-149.
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