A model-based approach to acoustic reflector localization with a robotic platform

Constructing a spatial map of an indoor environment, e.g., a typical office environment with glass surfaces, is a difficult and challenging task. Current state-of-the-art, e.g., camera- and laser-based approaches are unsuitable for detecting transparent surfaces. Hence, the spatial map generated with these approaches are often inaccurate. In this paper, a method that utilizes echolocation with sound in the audible frequency range is proposed to robustly localize the position of an acoustic reflector, e.g., walls, glass surfaces etc., which could be used to construct a spatial map of an indoor environment as the robot moves. The proposed method estimate the acoustic reflector’s position, using only a single microphone and a loudspeaker that are present on many socially assistive robot platforms such as the NAO robot. The experimental results show that the proposed method could robustly detect an acoustic reflector up to a distance of 1.5 m in more than 60% of the trials and works efficiently even under low SNRs. To test the proposed method, a proof-of-concept robotic platform was build to construct a spatial map of an indoor environment. This dataset is made available with IROS 2020 paper: https://ieeexplore.ieee.org/abstract/document/9341437 code could be found on Github: https://github.com/irtiq7/iROS2020构建室内环境（例如带有玻璃饰面的典型办公场景）的空间地图是一项极具挑战性的复杂任务。当前主流的前沿技术，如基于相机与激光的建图方案，无法有效检测透明表面，因此通过此类方法生成的空间地图往往精度欠佳。为此，本文提出一种利用可听频段声波回声定位的方法，以实现对声学反射体（如墙体、玻璃表面等）的稳健定位，可伴随移动机器人的行进过程构建室内环境的空间地图。 所提方法仅依托多数社交辅助机器人平台（如NAO机器人）标配的单麦克风与扬声器，即可估算声学反射体的位置。实验结果表明，该方法可在超过60%的测试轮次中，稳健检测1.5米范围内的声学反射体，且在低信噪比（Signal-to-Noise Ratio, SNR）条件下仍可高效运行。 为验证所提方法，本文搭建了概念验证型机器人平台以构建室内环境空间地图。本数据集随2020年国际智能机器人与系统会议（International Conference on Intelligent Robots and Systems, IROS）论文一同公开：https://ieeexplore.ieee.org/abstract/document/9341437，相关代码可在GitHub获取：https://github.com/irtiq7/iROS2020