🤖 AI Summary
This work addresses the challenge of achieving high accuracy and efficiency in distributed trajectory estimation for multi-robot systems under asynchronous communication and computation. The authors propose an asynchronous block coordinate descent algorithm based on sliding-window maximum a posteriori estimation. By incorporating a controllable approximation model, the method reduces communication overhead by up to 96.9% while introducing only negligible estimation error. Theoretical analysis establishes, for the first time, that the algorithm converges exponentially to the optimal solution under asynchronous iterations. Experimental results demonstrate that the proposed approach reduces estimation error by up to 64% compared to state-of-the-art methods and exhibits strong robustness across real-world robotic systems subjected to communication delays spanning three orders of magnitude.
📝 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 their 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 the robustness of this approach to delays whose lengths span three orders of magnitude.