On the Integration of Acoustics and LiDAR: a Multi-Modal Approach to Acoustic Reflector Estimation

TL;DR

This paper presents a multi-modal approach combining acoustics and LiDAR sensors to improve 3D acoustic reflector detection, addressing limitations of current methods in real-world scenarios with enhanced robustness and accuracy.

Contribution

The novel integration of LiDAR with microphone measurements enables better detection of acoustic reflectors in three dimensions, overcoming practical limitations of existing techniques.

Findings

LiDAR integration improves wall detection accuracy.

Multi-modal approach enhances robustness in challenging directions.

Simulation results confirm increased detection reliability.

Abstract

Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when employed in real-life scenarios. Detection of arbitrary reflectors in three dimensions encounters practical limitations, e.g., the need for a spherical array and the increased computational complexity. Moreover, loudspeakers may not have an omnidirectional directivity pattern, as usually assumed in the literature, making the detection of acoustic reflectors in some directions more challenging. In the proposed method, a LiDAR sensor is added to a loudspeaker to improve wall detection accuracy and robustness. This is done in two ways. First, the model mismatch introduced by horizontal reflectors can be resolved by detecting reflectors with the LiDAR sensor to enable elimination of their detrimental influence from the 2D problem in pre-processing. Second, a LiDAR-based method is proposed to compensate for the challenging directions where the directive loudspeaker emits little energy. We show via simulations that this multi-modal approach, i.e., combining microphone and LiDAR sensors, improves the robustness and accuracy of wall detection.