Asynchronous and Parallel Distributed Pose Graph Optimization
arXiv:2003.03281 · doi:10.1109/LRA.2020.3010216
Abstract
We present Asynchronous Stochastic Parallel Pose Graph Optimization (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, ASAPP offers resiliency against communication delays and alleviates the need to wait for stragglers in the network. Furthermore, 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 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 ASAPP's resilience against a wide range of delays in practice.
arXiv commentfull paper with appendices
References in corpus (12)
- HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
- Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization
- Global rates of convergence for nonconvex optimization on manifolds
- DOOR-SLAM: Distributed, Online, and Outlier Resilient SLAM for Robotic Teams
- How to Escape Saddle Points Efficiently
- An Asynchronous Parallel Stochastic Coordinate Descent Algorithm
- Asynchronous and Parallel Distributed Pose Graph Optimization
- A Riemannian low-rank method for optimization over semidefinite matrices with block-diagonal constraints
- Asynchronous Coordinate Descent under More Realistic Assumptions
- Efficiently escaping saddle points on manifolds
- Distributed Certifiably Correct Pose-Graph Optimization
- Generalized Proximal Methods for Pose Graph Optimization
Cited by in corpus (10)
- Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape
- Asynchronous and Parallel Distributed Pose Graph Optimization
- Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systems
- A Robot Web for Distributed Many-Device Localisation
- Distributed Simultaneous Localisation and Auto-Calibration using Gaussian Belief Propagation
- GeoD: Consensus-based Geodesic Distributed Pose Graph Optimization
- Kimera-Multi: a System for Distributed Multi-Robot Metric-Semantic Simultaneous Localization and Mapping
- A Survey of Distributed Optimization Methods for Multi-Robot Systems
- Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systems
- BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics