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Distributed Photometric Bundle Adjustment

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.

Open-Source Code

Coming soon: The source code is still being prepared and documented and will be released shortly. Feel free to watch the repository to get a notification when it becomes available: https://github.com/tum-vision/dbatk

Publication


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Conference and Workshop Papers
2020
[]Distributed Photometric Bundle Adjustment (N Demmel, M Gao, E Laude, T Wu and D Cremers), In International Conference on 3D Vision (3DV), 2020. ([project page][code]) [bibtex] [pdf]Oral Presentation
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03.04.2022

In April 2022 Jürgen Sturm, Christian Kerl and Daniel Cremers were featured among the top 10 most influential scholars in robotics of the last decade.

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