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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Vydáno v:	IEEE robotics and automation letters Ročník 5; číslo 4; s. 5819 - 5826
Hlavní autoři:	Tian, Yulun, Koppel, Alec, Bedi, Amrit Singh, How, Jonathan P.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.10.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
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Abstract	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.
AbstractList	We present Asynchronous Stochastic Parallel Pose Graph Optimization ([Formula Omitted]), 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, [Formula Omitted] offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, [Formula Omitted] 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 [Formula Omitted] 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 [Formula Omitted]'s resilience against a wide range of delays in practice. 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.
Author	Tian, Yulun How, Jonathan P. Bedi, Amrit Singh Koppel, Alec
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SubjectTerms	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
Title	Asynchronous and Parallel Distributed Pose Graph Optimization
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