Asynchronous and Parallel Distributed Pose Graph Optimization

We present Asynchronous Stochastic Parallel Pose Graph Optimization (<inline-formula><tex-math notation="LaTeX">\textsc {ASAPP}</tex-math></inline-formula>), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous l...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 5; no. 4; pp. 5819 - 5826
Main Authors: Tian, Yulun, Koppel, Alec, Bedi, Amrit Singh, How, Jonathan P.
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer simulation
Convergence
Delay
Delays
distributed robot systems
localization
Manifolds
mapping
Multiple robots
Optimization
optimization and optimal control
Resilience
Robot kinematics
Simultaneous localization and mapping
SLAM
Synchronism
Trajectory analysis
Trajectory optimization
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:We present Asynchronous Stochastic Parallel Pose Graph Optimization (<inline-formula><tex-math notation="LaTeX">\textsc {ASAPP}</tex-math></inline-formula>), the first asynchronous algorithm for distributed pose graph optimization (PGO) in multi-robot simultaneous localization and mapping. By enabling robots to optimize their local trajectory estimates without synchronization, <inline-formula><tex-math notation="LaTeX">\textsc {ASAPP}</tex-math></inline-formula> offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, <inline-formula><tex-math notation="LaTeX">\textsc {ASAPP}</tex-math></inline-formula> can be applied on the rank-restricted relaxations of PGO, a crucial class of non-convex Riemannian optimization problems that underlies recent breakthroughs on globally optimal PGO. Under bounded delay, we establish the global first-order convergence of <inline-formula><tex-math notation="LaTeX">\textsc {ASAPP}</tex-math></inline-formula> using a sufficiently small stepsize. The derived stepsize depends on the worst-case delay and inherent problem sparsity, and furthermore matches known result for synchronous algorithms when there is no delay. Numerical evaluations on simulated and real-world datasets demonstrate favorable performance compared to state-of-the-art synchronous approach, and show <inline-formula><tex-math notation="LaTeX">\textsc {ASAPP}</tex-math></inline-formula>'s resilience against a wide range of delays in practice.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2020.3010216