Wi-Closure: Reliable and Efficient Search of Inter-robot Loop Closures Using Wireless Sensing

TL;DR

Wi-Closure is a novel wireless sensing-based algorithm that significantly improves the efficiency and robustness of multi-robot loop closure detection in SLAM, reducing computation time and error without sacrificing accuracy.

Contribution

The paper introduces Wi-Closure, a new method that leverages wireless signals to prune potential loop closures, enhancing multi-robot SLAM performance in complex environments.

Findings

Reduces computation time by up to 77% in hardware experiments.

Decreases trajectory estimation error by up to 99% in simulation.

Maintains accuracy while improving robustness in challenging environments.

Abstract

In this paper we propose a novel algorithm, Wi-Closure, to improve computational efficiency and robustness of loop closure detection in multi-robot SLAM. Our approach decreases the computational overhead of classical approaches by pruning the search space of potential loop closures, prior to evaluation by a typical multi-robot SLAM pipeline. Wi-Closure achieves this by identifying candidates that are spatially close to each other by using sensing over the wireless communication signal between robots, even when they are operating in non-line-of-sight or in remote areas of the environment from one another. We demonstrate the validity of our approach in simulation and hardware experiments. Our results show that using Wi-closure greatly reduces computation time, by 54% in simulation and by 77% in hardware compared, with a multi-robot SLAM baseline. Importantly, this is achieved without sacrificing accuracy. Using Wi-Closure reduces absolute trajectory estimation error by 99% in simulation and 89.2% in hardware experiments. This improvement is due in part to Wi-Closure's ability to avoid catastrophic optimization failure that typically occurs with classical approaches in challenging repetitive environments.