On the Integration of Acoustics and LiDAR

On the Integration of Acoustics and LiDAR: A LiDAR-aided approach for detection of acoustically reflective surfaces from microphone measurements

Riemens, Ellen (TU Delft Electrical Engineering, Mathematics and Computer Science)

Martinez, Jorge (graduation committee)
Hendriks, R.C. (mentor)

Delft University of Technology

Electrical Engineering | Circuits and Systems

2021-09-06

Loudspeakers are placed in an environment unknown to the loudspeaker designers. The room influences the acoustic experience for the user. Having information about the room makes it possible to better reproduce the sound field as intended. Using microphone measurements, the location of acoustic reflectors can be inferred. Current state-of-the-art methods for room boundary detection focus on a two-dimensional setting. Detection of arbitrary reflectors in three dimensions increase complexity due to practical limitations, i.e. the need for a spherical array and the increase of computational complexity. The presence of horizontal reflectors cause inaccuracy for wall detection due to model mismatch. Loudspeakers may not present an omnidirectional directivity pattern, as usually assumed in the literature, thus making the detection of acoustic reflectors in some directions more challenging.

In this thesis, a LiDAR sensor is added to a smart loudspeaker to improve wall detection accuracy and robustness. This is done in two ways.
First, the horizontal reflectors that are not present in the acoustic model are sought detected with the LiDAR sensor to enable elimination of their detrimental influence. Second, a method is proposed to compensate for the challenging regions for wall detection in highly directive loudspeakers, using the LiDAR sensor. Experimental results, evaluated in different simulated scenarios are shown for comparison of the proposed method and the state-of-the-art method, that exclusively uses acoustic information.

http://resolver.tudelft.nl/uuid:285e4009-9384-4350-8eb4-d2ddbae4c333

Student theses

master thesis

© 2021 Ellen Riemens