3D Localization of a Sound Source Using Mobile Microphone Arrays Referenced by SLAM

Michaud, Simon; Faucher, Samuel; Grondin, François; Lauzon, Jean-Samuel; Labbé, Mathieu; Létourneau, Dominic; Ferland, François; Michaud, François

doi:10.1109/iros45743.2020.9341098

Cited by 16 publications

(12 citation statements)

References 27 publications

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“…It does not work on robot selflocalization and environment mapping. A recent work [21] proposed to use two moving microphone arrays to do SSL separately and to estimate the sound source location using the intersection of sound source direction extension lines.…”

Section: B Audio-visual Fusion Methodsmentioning

confidence: 99%

AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments

Zhang

et al. 2021

Preprint

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Dynamic objects in the environment, such as people and other agents, lead to challenges for existing simultaneous localization and mapping (SLAM) approaches. To deal with dynamic environments, computer vision researchers usually apply some learning-based object detectors to remove these dynamic objects. However, these object detectors are computationally too expensive for mobile robot on-board processing. In practical applications, these objects output noisy sounds that can be effectively detected by on-board sound source localization. The directional information of the sound source object can be efficiently obtained by direction of sound arrival (DoA) estimation, but the depth estimation is difficult. Therefore, in this paper, we propose a novel audio-visual fusion approach that fuses sound source direction into the RGB-D image and thus removes the effect of dynamic obstacles on the multi-robot SLAM system. Experimental results of multirobot SLAM in different dynamic environments show that the proposed method uses very small computational resources to obtain very stable self-localization results.

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Section: B Audio-visual Fusion Methodsmentioning

confidence: 99%

AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments

Zhang

et al. 2021

Preprint

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“…For both setups, the sound card 16SoundsUSB 5 performs signal acquisitions and then ODAS performs SSL, SST and SSS. Figure 9 illustrates the 16 microphones configuration on the SecurBot robots Michaud et al (2020) . ODAS is also used with the Beam robot Laniel et al (2017) , placing eight microphones on the same plane and using the 8SoundsUSB sound card 6 .…”

Section: Applicationsmentioning

confidence: 99%

ODAS: Open embeddeD Audition System

et al. 2022

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“…For both setups, the sound card 16SoundsUSB 5 performs signal acquisitions and then ODAS performs SSL, SST and SSS. Figure 7 illustrates the 16 microphones configuration on the SecurBot robots [30]. ODAS is also used with the Beam robot [31], placing 8 microphones on the same plane and using the 8SoundsUSB sound card 6 .…”

Section: ) Localizationmentioning

confidence: 99%

ODAS: Open embeddeD Audition System

Grondin¹,

Létourneau²,

Godin³

et al. 2021

Preprint

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Artificial audition aims at providing hearing capabilities to machines, computers and robots. Existing frameworks in robot audition offer interesting sound source localization, tracking and separation performance, but involve a significant amount of computations that limit their use on robots with embedded computing capabilities. This paper presents ODAS, the Open embeddeD Audition System framework, which includes strategies to reduce the computational load and perform robot audition tasks on low-cost embedded computing systems. It presents key features of ODAS, along with cases illustrating its uses in different robots and artificial audition applications. I. INTRODUCTIONSimilarly to artificial/computer vision, artificial/computer audition can be defined as the ability to provide hearing capabilities to machines, computers and robots. Vocal assistants on smart phones and smart speakers are now common, providing a vocal interface between people and devices [1]. But as for artificial vision, there are still many problems to resolve for endowing robots with adequate hearing capabilities, such as ego and non-stationary noise cancellation, mobile and distant speech and sound understanding [2]- [6].Open source software frameworks, such as OpenCV [7] for vision and ROS [8] for robotics, greatly contribute in making these research fields evolve and progress, allowing the research community to share and mutually benefit from collective efforts. In artificial audition, two main frameworks exist: • HARK (Honda Research Institute Japan Audition for Robots with Kyoto University 1 ) provides multiple modules for sound source localization and separation [9]-[11]. This framework is mostly built over the FlowDesigner software [12], and can also be interfaced with speech recognition tools such as Julius [13] and Kaldi [14], [15]. HARK implements sound source localization in 2-D using variants of the Multiple Signal Classification (MUSIC) algorithm [16]-[18]. HARK also performs geometrically-constrained higher-order decorrelation-based source separation with adaptive *This work was supported by FRQNT -Fonds recherche Québec Nature et Technologie.

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3D Localization of a Sound Source Using Mobile Microphone Arrays Referenced by SLAM

Cited by 16 publications

References 27 publications

AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments

AcousticFusion: Fusing Sound Source Localization to Visual SLAM in Dynamic Environments

ODAS: Open embeddeD Audition System

ODAS: Open embeddeD Audition System

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