An Asynchronous Parallel Algorithm Framework for Decentralized Pose Graph Optimization

This paper proposes an asynchronous parallel algorithm framework for decentralized pose graph optimization (PGO) in collaborative simultaneous localization and mapping. Our framework combines and generalizes the superiority of the existing works on PGO. Specifically, we decompose the objective funct...

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Veröffentlicht in:2023 3rd International Conference on Computer, Control and Robotics (ICCCR) S. 158 - 163
Hauptverfasser: Guo, Guanghui, Yi, Peng
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 24.03.2023
Schlagworte:
Delays
Distributed Optimization
Manifolds
Optimization methods
Pose Graph Optimization
Riemannian Mani-fold
Search problems
Simultaneous localization and mapping
Transforms
Turning
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Zusammenfassung:This paper proposes an asynchronous parallel algorithm framework for decentralized pose graph optimization (PGO) in collaborative simultaneous localization and mapping. Our framework combines and generalizes the superiority of the existing works on PGO. Specifically, we decompose the objective function of decentralized PGO into the sum of multiple sub-problems to be solved in a fully distributed fashion, and reformulate the sub-problems with the rank-restricted relaxation strategy to reduce the size of the search space and provide a computational certifiable. Moreover, the proposed framework is Gauss-Seidel like without compromising convergence and accelerates the optimization inference with an asynchronous parallel scheme. Finally, we experimentally evaluate the performance of our framework embedded with different optimizers on bench-mark PGO datasets under bounded communication delays. The experiment results demonstrate that, compared to the existing methods, our framework has a faster converge without additional parameter turning and a strong resilience for communication delays.
DOI:10.1109/ICCCR56747.2023.10193900