Asynchronous and Parallel Distributed Pose Graph Optimization
Name
2003.03281.pdf
Description
Accepted version
Size
672.11 KB
Format
Adobe PDF
Checksum (MD5)
9f56819e58b978a1e80a420d98bdc095
Author(s)
Tian, Yulun
Koppel, Alec
Bedi, Amrit Singh
How, Jonathan P
Date Issued
2020
Journal
IEEE Robotics and Automation Letters
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
© 2016 IEEE. We present Asynchronous Stochastic Parallel Pose Graph Optimization ($\textsc {ASAPP}$), 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, $\textsc {ASAPP}$ offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, $\textsc {ASAPP}$ 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 $\textsc {ASAPP}$ 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 $\textsc {ASAPP}$'s resilience against a wide range of delays in practice.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/LRA.2020.3010216
