Abstract
An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to, and interact with sound sources of interest. Acoustic scene mapping creates a three-dimensional (3D) representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival (DoA) estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moreover, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping (aSLAM), which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources while passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy.
Highlights
S IMULTANEOUS Localization and Mapping (SLAM) localizes an unknown, moving observer and jointly maps the 3D positions of objects of interest in the vicinity
The SLAM problem is fully described by the posterior pdf, p(rt, St | y1:t, Ω1:t ), which can be factorized into the observer posterior pdf, p(rt | y1:t, Ω1:t ), and conditional multi-source posterior pdf, p(St | rt, Ω1:t ): p = p p ( St | rt, Ω1:t ) . (11)
To ensure that the posterior source Probability Hypothesis Density (PHD) corresponds to a stable distribution, the birth PHD is modelled by a Gaussian Mixture Model (GMM): Mt Jb λb =
Summary
S IMULTANEOUS Localization and Mapping (SLAM) localizes an unknown, moving observer and jointly maps the 3D positions of objects of interest in the vicinity. By virtue of the universal presence of immovable fixtures in visual scenes, FActored Solution To Simultaneous Localization and Mapping (FastSLAM) aligns the observer using permanently visible landmarks This prerequisite is fundamentally conflicting with acoustic signals, affected by speech inactivity and reverberation. We propose a novel approach, named Acoustic SLAM (aSLAM), to map the positions of sound sources passively, and simultaneously localize a moving observer in realistic acoustic environments. The novel features of the proposed aSLAM approach are 1) the joint estimation of the unknown observer path and the positions of multiple interfering sound sources, that is 2) robust against reverberation, noise, and periods of source inactivity, and uses 3) passive acoustic sensor arrays.
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