I-ASM: Iterative Acoustic Scene Mapping for Enhanced Robot Auditory Perception in Complex Indoor Environments

This paper addresses the challenge of acoustic scene mapping (ASM) in complex indoor environments with multiple sound sources. Unlike existing methods that rely on prior data association or SLAM frameworks, we propose a novel particle filter-based iterative framework, termed I-ASM, for ASM using a mobile robot equipped with a microphone array and LiDAR. I-ASM harnesses an innovative "implicit association" to align sound sources with Direction of Arrival (DoA) observations without requiring explicit pairing, thereby streamlining the mapping process. Given inputs including an occupancy map, DoA estimates from various robot positions, and corresponding robot pose data, I-ASM performs multi-source mapping through an iterative cycle of "Filtering-Clustering-Implicit Associating". The proposed framework has been tested in real-world scenarios with up to 10 concurrent sound sources, demonstrating its robustness against missing and false DoA estimates while achieving high-quality ASM results. To benefit the community, we open-source all the codes and data at https://github.com/AISLAB-sustech/Acoustic-Scene-Mapping