Asynchronous and Parallel Distributed Pose Graph Optimization

A recent paper by members of the DCIST alliance has received a 2020 honorable mention from IEEE Robotics and Automation Letters.

The paper presents 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, the authors 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.

Source: Yulun Tian, Alec Koppel, Amrit Singh Bedi, and Jonathan P. How, “Asynchronous and Parallel Distributed Pose Graph Optimization,” in IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5819-5826, Oct. 2020.

More information: