Sonar-based indoor mapping systems have been widely employed in robotics for several decades. While such systems are still the mainstream in underwater and pipe inspection settings, the vulnerability to noise reduced, over time, their general widespread usage in favour of other modalities(\textit{e.g.}, cameras, lidars), whose technologies were encountering, instead, extraordinary advancements. Nevertheless, mapping physical environments using acoustic signals and echolocation can bring significant benefits to robot navigation in adverse scenarios, thanks to their complementary characteristics compared to other sensors. Cameras and lidars, indeed, struggle in harsh weather conditions, when dealing with lack of illumination, or with non-reflective walls. Yet, for acoustic sensors to be able to generate accurate maps, noise has to be properly and effectively handled. Traditional signal processing techniques are not always a solution in those cases. In this paper, we propose a framework where machine learning is exploited to aid more traditional signal processing methods to cope with background noise, by removing outliers and artefacts from the generated maps using acoustic sensors. Our goal is to demonstrate that the performance of traditional echolocation mapping techniques can be greatly enhanced, even in particularly noisy conditions, facilitating the employment of acoustic sensors in state-of-the-art multi-modal robot navigation systems. Our simulated evaluation demonstrates that the system can reliably operate at an SNR of $-10$dB. Moreover, we also show that the proposed method is capable of operating in different reverberate environments. In this paper, we also use the proposed method to map the outline of a simulated room using a robotic platform.

翻译：基于声纳的室内建图系统已在机器人领域广泛应用数十年。尽管此类系统在水下和管道检测场景中仍是主流技术，但其对噪声的敏感性逐渐削弱了其普遍适用性，而其他模态（如相机、激光雷达）技术却取得了显著进步。然而，利用声信号与回声定位进行物理环境建图，凭借其相对于其他传感器的互补特性，仍能为恶劣场景下的机器人导航带来重要优势。事实上，相机与激光雷达在极端天气、光照不足或非反射墙面条件下性能受限。但要使声学传感器能够生成精确地图，必须妥善处理噪声问题。传统信号处理方法在此类场景中并非总是有效。本文提出一种框架，通过机器学习辅助传统信号处理方法应对背景噪声，利用声学传感器从生成的地图中剔除异常值与伪影。我们的目标是证明即使在强噪声条件下，传统回声定位建图技术的性能也能获得显著提升，从而促进声学传感器在先进多模态机器人导航系统中的应用。仿真实验表明，该系统能在信噪比低至$-10$dB的环境中可靠运行。此外，我们还验证了所提方法在不同混响环境中的适应性。本文进一步通过机器人平台，应用该方法对模拟房间轮廓进行了建图验证。