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.

翻译：在本文中,我们提出了一个新颖的算法,即Wi-Closure,目的是提高多机器人SLAM中循环封闭探测的计算效率和稳健性。我们的方法通过在典型的多机器人SLAM输油管前对潜在循环封闭的搜索空间进行处理,从而降低古典方法的计算间接费用。Wi-Closure通过在机器人之间的无线通信信号上进行感测,从而确定在空间上彼此相近的候选人,即使在机器人在非视线或环境的偏远地区运行时也是如此。我们展示了我们在模拟和硬件实验中的方法的有效性。我们的结果显示,使用Wi-Closure将计算时间大大减少,在模拟中使用54%,在硬件中使用77%。重要的是,这是在不牺牲准确性的情况下实现的。使用Wi-Closilus在模拟中将绝对轨道估计错误减少99%,在硬件实验中减少89.2%。这一改进部分是由于Wi-Clos在避免灾难性优化失败方面的能力,通常在挑战重复性环境中的传统方法中出现这种情况。