A Model-based Approach to Acoustic Reflector Localization with a Robotic Platform

Saqib, Usama; Jensen, Jesper Rindom

doi:10.1109/iros45743.2020.9341437

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…Adapting this method to a moving setup, the authors of [12] map out the room, one wall at a time, using a small loudspeaker and highquality microphones mounted on a wheeled robot. The authors of [13] use a similar platform but recover the peaks of the RIR through nonlinear least-squares optimization on the wide-band frequency response. In follow-up work [14], this method is extended with a DOA estimator.…”

Section: Related Workmentioning

confidence: 99%

Blind as a Bat: Audible Echolocation on Small Robots

Dümbgen

Hoffet

Kolundžija

et al. 2023

IEEE Robot. Autom. Lett.

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For safe and efficient operation, mobile robots need to perceive their environment, and in particular, perform tasks such as obstacle detection, localization, and mapping. Although robots are often equipped with microphones and speakers, the audio modality is rarely used for these tasks. Compared to the localization of sound sources, for which many practical solutions exist, algorithms for active echolocation are less developed and often rely on hardware requirements that are out of reach for small robots.We propose an end-to-end pipeline for sound-based localization and mapping that is targeted at, but not limited to, robots equipped with only simple buzzers and low-end microphones. The method is model-based, runs in real time, and requires no prior calibration or training. We successfully test the algorithm on the e-puck robot with its integrated audio hardware, and on the Crazyflie drone, for which we design a reproducible audio extension deck. We achieve centimeter-level wall localization on both platforms when the robots are static during the measurement process. Even in the more challenging setting of a flying drone, we can successfully localize walls, which we demonstrate in a proof-of-concept multi-wall localization and mapping demo.

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Section: Related Workmentioning

confidence: 99%

Blind as a Bat: Audible Echolocation on Small Robots

Dümbgen

Hoffet

Kolundžija

et al. 2023

IEEE Robot. Autom. Lett.

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“…The estimation the impulse response in the presence of noise can be improved [25], [26] by using a maximum likelihood (ML) (or expectation maximization (EM), or nonlinear leastsquares (NLS)) optimization method [27]. This method has been applied to localization using a single microphone [28] and an array of microphones, and the effects of signal to noise ratio, hardware transfer function [29], correlated and coloured noise, and faulty microphones [25] have been considered. Recent work has used acoustic interference to localize walls close to a small, resource constrained robot [30], showing precise localization at a distance up to around 0.5 metres.…”

Section: A Related Work In Acoustic Echo Localizationmentioning

confidence: 99%

Acoustic Echo Sensing for Robot Localization in Buried Pipe Networks

Worley¹,

Yu²,

Horoshenkov³

et al. 2023

Preprint

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<p>The abstract of this pre-print paper is: Robot localization in buried pipes presents a number of challenges, including the unavailability of methods such as a global positioning system, and the limited perspective of sensors such as vision. This paper addresses these challenges by using acoustic sensing, where signals sent by robots propagate long distances and around corners in the pipe environment, and can be used to estimate the distance to reflective features. It is shown that the reverberant environment causes a number of echoes which contain useful information. A novel algorithm for using this information in pose-graph optimization is proposed, and is shown to be necessary in comparison to a naive approach. The algorithm is demonstrated to be accurate and robust by measuring the error rate, which is the proportion of estimate error that was greater than a target threshold of 0.5 metres. The median error rate was 0 for measurement uncertainty 2.5 times larger than that found experimentally, and for motion uncertainty 4 times that which could be tolerated by an idealised loop-closure approach used for comparison. This work is the foundation for new data fusion methods for robot localization in buried pipes.</p>

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“…This relaxed problem can then be addressed by the sliding Frank-Wolfe algorithm, a greedy approach proposed by [52], that belongs to the broader class of so-called super-resolution or gridless techniques. We adapted this method to tackle problem (7) in [47], and were able to accurately recover hundreds of image sources within range, for large enough sampling frequencies and array sizes. Importantly, the recovered image sources are unlabeled, some may be missing, and false positives or mislocated sources may exist.…”

Section: B Source Recoverymentioning

confidence: 99%

“…H EARING the shape of a room, or more formally the problem of recovering the properties of a room boundary from the acoustic measurements of one or several sound sources inside of it, is a difficult inverse problem that has intrigued researchers in audio signal processing and room acoustics for many years. Beyond its folklore nature, solutions to this problem could benefit applications in augmented reality [1], [2], room compensation [3], sound field reconstruction [4], [5], robotic navigation [6], [7], or room acoustic diagnosis [8].…”

Section: Introductionmentioning

confidence: 99%

Gridless 3D Recovery of Image Sources From Room Impulse Responses

Sprunck

Privat

Foy³

et al. 2022

IEEE Signal Process. Lett.

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We present an algorithm that fully reverses the shoebox image source method (ISM), a popular and widely used room impulse response (RIR) simulator for cuboid rooms introduced by Allen and Berkley in 1979. More precisely, given a discrete multichannel RIR generated by the shoebox ISM for a microphone array of known geometry, the algorithm reliably recovers the 18 input parameters. These are the 3D source position, the 3 dimensions of the room, the 6-degrees-of-freedom room translation and orientation, and an absorption coefficient for each of the 6 room boundaries. The approach builds on a recently proposed gridless image source localization technique combined with new procedures for room axes recovery and firstorder-reflection identification. Extensive simulated experiments reveal that near-exact recovery of all parameters is achieved for a 32-element, 8.4-cm-wide spherical microphone array and a sampling rate of 16 kHz using fully randomized input parameters within rooms of size 2×2×2 to 10×10×5 meters. Estimation errors decay towards zero when increasing the array size and sampling rate. The method is also shown to strongly outperform a known baseline, and its ability to extrapolate RIRs at new positions is demonstrated. Crucially, the approach is strictly limited to low-passed discrete RIRs simulated using the vanilla shoebox ISM. Nonetheless, it represents to our knowledge the first algorithmic demonstration that this difficult inverse problem is in-principle fully solvable over a wide range of configurations.

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A Model-based Approach to Acoustic Reflector Localization with a Robotic Platform

Cited by 7 publications

References 17 publications

Blind as a Bat: Audible Echolocation on Small Robots

Blind as a Bat: Audible Echolocation on Small Robots

Acoustic Echo Sensing for Robot Localization in Buried Pipe Networks

Gridless 3D Recovery of Image Sources From Room Impulse Responses

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