Technical Report: Asynchronous Distributed Trajectory Estimation of Multi-Robot Systems
arXiv:2607.01106
The paper introduces an asynchronous block coordinate descent method for distributed trajectory estimation in multi‑robot teams, achieving large communication savings and provable exponential convergence, with demonstrated accuracy improvements in simulation and real‑robot experiments.
Abstract
Distributed trajectory estimation arises in many applications across robotics, but existing implementations typically do not consider asynchrony in agents' communications and computations. Therefore, we propose an asynchronous block coordinate descent algorithm for distributed trajectory estimation. We consider a team of agents that observes a team of robots and estimates the robots' states over a sliding window. The agents solve an approximation of the maximum a posteriori estimation problem, which we derive. We show this approximation introduces negligible errors and eliminates up to 96.9% of communications among agents. Next, we prove that agents' iterates converge exponentially fast to the optimal estimate of the robots' states. Simulations show that this approach has up to 64% less error than a comparable state-of-the-art algorithm. Experiments on mobile robots show this approach is robust to delays whose lengths span three orders of magnitude.
13 pages, 3 figures