Simultaneous Localization of a Mobile Robot and Multiple Sound Sources Using a Microphone Array

Hu, Jwu‐Sheng; Chan, Chen Yu; Wang, Chengkang; Lee, Ming Tang; Kuo, Ching Yi

doi:10.1163/016918610x538525

Cited by 49 publications

(43 citation statements)

References 9 publications

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“…Camera-based (optical) systems [6], [7] and most acoustic systems using microphones [8], [9], [10], [11], [12] try to localize a source by sensing its signal through multiple receivers with known positions. Such systems can potentially violate user privacy by recording their video or voices without explicit approval.…”

Section: Related Workmentioning

confidence: 99%

Indoor localization using controlled ambient sounds

Rishabh

Kimber

Adcock

2012

2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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Abstract-Audio-based receiver localization in indoor environments has multiple applications including indoor navigation, location tagging, and tracking. Public places like shopping malls and consumer stores often have loudspeakers installed to play music for public entertainment. Similarly, office spaces may have sound conditioning speakers installed to soften other environmental noises. We discuss an approach to leverage this infrastructure to perform audio-based localization of devices requesting localization in such environments, by playing barely audible controlled sounds from multiple speakers at known positions. Our approach can be used to localize devices such as smart-phones, tablets and laptops to sub-meter accuracy. The user does not need to carry any specialized hardware. Unlike acoustic approaches which use high-energy ultrasound waves, the use of barely audible (low energy) signals in our approach poses very different challenges. We discuss these challenges, how we addressed those, and experimental results on two prototypical implementations: a request-play-record localizer, and a continuous tracker. We evaluated our approach in a real world meeting room and report promising initial results with localization accuracy within half a meter 94% of the time. The system has been deployed in multiple zones of our office building and is now part of a location service in constant operation in our lab.

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Section: Related Workmentioning

confidence: 99%

Indoor localization using controlled ambient sounds

Rishabh

Kimber

Adcock

2012

2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

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“…Prior work has considered visual [1,2,3], range-only [4,5], and acoustic SLAM [6], as well as solutions based on multiple sensor modalities [7]. In [6], the authors propose a framework to simultaneously localizes the mobile robot and multiple sound sources using a microphone array on the robot.…”

Section: Introductionmentioning

confidence: 99%

“…In [6], the authors propose a framework to simultaneously localizes the mobile robot and multiple sound sources using a microphone array on the robot. Echoes and multipath have been used previously to do SLAM [8,9], and for room geometry estimation in general [10,11].…”

Section: Introductionmentioning

confidence: 99%

EchoSLAM: Simultaneous localization and mapping with acoustic echoes

Kreković

Dokmanić

Vetterli

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We address the problem of jointly localizing a robot in an unknown room and estimating the room geometry from echoes. Unlike earlier work using echoes, we assume a completely autonomous setup with (near) collocated microphone and the acoustic source. We first introduce a simple, easy to analyze estimator, and prove that the sequence of room and trajectory estimates converges to the true values. Next, we approach the problem from a Bayesian point of view, and propose a more general solution which does not require any assumptions on motion and measurement model of the robot. In addition to theoretical analysis, we validate both estimators numerically.

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“…Recent work on Acoustic SLAM (a-SLAM) using speech signals is limited to only a few examples including [5,6]. This is perhaps due to the very signficant challenges of SSL and tracking in realistic environments.…”

Section: Introductionmentioning

confidence: 99%

Acoustic simultaneous localization and mapping (A-SLAM) of a moving microphone array and its surrounding speakers

Evers

Moore

Naylor

2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Acoustic scene mapping creates a representation of positions of audio sources such as talkers within the surrounding environment of a microphone array. By allowing the array to move, the acoustic scene can be explored in order to improve the map. Furthermore, the spatial diversity of the kinematic array allows for estimation of the source-sensor distance in scenarios where source directions of arrival are measured. As sound source localization is performed relative to the array position, mapping of acoustic sources requires knowledge of the absolute position of the microphone array in the room. If the array is moving, its absolute position is unknown in practice. Hence, Simultaneous Localization and Mapping (SLAM) is required in order to localize the microphone array position and map the surrounding sound sources. In realistic environments, microphone arrays receive a convolutive mixture of direct-path speech signals, noise and reflections due to reverberation. A key challenge of Acoustic SLAM (a-SLAM) is robustness against reverberant clutter measurements and missing source detections. This paper proposes a novel bearing-only a-SLAM approach using a Single-Cluster Probability Hypothesis Density filter. Results demonstrate convergence to accurate estimates of the array trajectory and source positions.

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Simultaneous Localization of a Mobile Robot and Multiple Sound Sources Using a Microphone Array

Cited by 49 publications

References 9 publications

Indoor localization using controlled ambient sounds

Indoor localization using controlled ambient sounds

EchoSLAM: Simultaneous localization and mapping with acoustic echoes

Acoustic simultaneous localization and mapping (A-SLAM) of a moving microphone array and its surrounding speakers

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