Robust Localization of Mobile Robot in Reverberant Rooms Using Acoustic Beacons with Iterative Bayesian Filtering

Ogiso, Satoki; Mizutani, Koichi; Wakatsuki, Naoto; Ebihara, Tadashi

doi:10.1109/ipin.2018.8533824

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Their experiments were carried out in largely noise-saturated environments. In the domain of robotics for indoor localisation Ogiso et al [113] used a robot-mounted microphone array to attain positioning information of a pre-defined track. The robot would move in an 6m × 6m arena enclosed by four sources of sound, achieving sub-meter performance.…”

Section: Ultrasonic and Acoustic Sensorsmentioning

confidence: 99%

“…Ultrasound and acoustic sensors offer great precision but only at short ranges and in LoS laboratory conditions [113,122]. Interestingly, most of the studies included in this survey have indicated that aside from these shortcomings, ultrasound is mostly preferred due to its low-cost and ability to reuse already existing sensor infrastructure, such as smartphones [110].…”

Section: Drawbacks and Modality Evaluationmentioning

confidence: 99%

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Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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The importance of accurate and efficient positioning and tracking is widely understood. However, there is a pressing lack of progress in the standardisation of methods, as well as generalised framework of their evaluation. The aim of this survey is to discuss the currently prevalent and emerging types of sensors used for location estimation. The intent of this review is to take account of this taxonomy and to provide a wider understanding of the current state-of-the-art. To that end, we outline various sensor modalities, as well as popular fusion and integration techniques, discussing how their combinations can help in various application settings. Firstly, we present the fundamental mechanics behind sensors employed by the localisation community. Furthermore we outline the formal theory behind prominent fusion methods and provide exhaustive implementation examples of each. Finally, we provide points for future discussion regarding localisation sensing, fusion and integration methods.to user's preference and experience. This makes the relative selection space large, and frequently open to interpretation with regard to available resources and constraints.Whilst the survey literature pertaining to localisation systems and methods is large [54,171,181], there exists very little in the way of localisation-centric sensor utilisation. This encompasses the use of bespoke [39] or off-the-shelf [13,118] sensors, specifically for the use of location estimation, robustification and optimisation. This area is extensive [20,35,57,83,136,146], yet very often bundled along with localisation technology surveys, without subsequent scrutiny. We aim to close this gap, by reviewing sensors, their fusion and utilisation as applied to localisation, in contrast to localisation methods, technologies and implementations themselves.Most of the existing localisation surveys include technology-specific reviews [30,54,92,181]. They concentrate upon the methods and algorithms related to indoor localisation [30,92], techniques and technologies [181]. Some work also addresses localisation from the perspective of the device itself, such as smartphones [171]. Xiao et al. study [171] is the most closely related work to our proposed examination. The main difference is, that instead of reviewing the devices as

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Section: Ultrasonic and Acoustic Sensorsmentioning

confidence: 99%

Section: Drawbacks and Modality Evaluationmentioning

confidence: 99%

Sensor Modalities and Fusion for Robust Indoor Localisation

Kozłowski¹,

Santos-Rodríguez²,

Piechocki³

2019

ICST Transactions on Ambient Systems

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“…The proposed method does not require the deterministic DOA estimation, but rather evaluates location candidates by location likelihood. The proposed method was previously suggested to be robust in a reverberant environment [35], but the location likelihood itself was not discussed and the robustness has not been verified with experimental results such as NLOS or strong reflective waves. In this paper, the proposed method was evaluated using three experiments and the results were discussed with the location likelihood maps for each experiment.…”

Section: Introductionmentioning

confidence: 98%

Robust Indoor Localization in a Reverberant Environment Using Microphone Pairs and Asynchronous Acoustic Beacons

et al. 2019

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In this paper, a robust indoor localization method using microphone pairs and asynchronous acoustic beacons was proposed. The proposed method is applicable even with a two-channel microphone pair, which is the minimal configuration of a microphone array. The proposed method estimates location by using the cross-correlation functions of the measured signals as location likelihoods. Three experiments were conducted to evaluate the proposed method. Four beacons were located at the corners of a localizing area of 4 m by 4 m and emitted signals with a bandwidth of 2 kHz. The localization results were compared to the previous method with deterministic direction-of-arrival estimation. The 90th percentiles of the localization error were 0.23 m for the proposed method with two microphones, 0.19 m for the proposed method with four microphones, and 0.30 m for the previous method under conditions without significant reverberation. Under a condition with reflective walls, the 90th percentile of the localization error of the previous method increased to 0.49 m, while that of the proposed method was only increased to 0.23 m for two microphones and 0.19 m for four microphones. The proposed method contributes to a robust localization in indoor environments and relieves the constraints of receiver configuration.INDEX TERMS Acoustic beacons, cross correlation, indoor localization, microphone pairs, particle filter.

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Study on passive location method of shallow water acoustic source with single hydrophone

Shi

Chang

2022

西北工业大学学报

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In order to meet the needs of target detection in shallow water with concealed and low energy consumption platform, the Bayesian estimation theory was applied to underwater acoustic location. The underwater acoustic location model was established based on probability density function as the description of sound source state, so as to overcome the problems of unstable shallow water environment and mismatching of sound field model. Histogram filtering method is used to solve the integral solution in the process of posterior probability estimation of sound source state. The hierarchical grid histogram filtering method is proposed for the first time, which effectively improves the efficiency of histogram filtering iterative algorithm. The measured data and simulation results of SWelleX-96 show that the depth positioning accuracy can reach 35 m and the distance positioning accuracy can reach 0.69 km within the range of 200 m deep and 10 km long in shallow water environment, and the efficiency of the algorithm can be improved by N1/2 times.

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Robust Localization of Mobile Robot in Reverberant Rooms Using Acoustic Beacons with Iterative Bayesian Filtering

Cited by 5 publications

References 18 publications

Sensor Modalities and Fusion for Robust Indoor Localisation

Sensor Modalities and Fusion for Robust Indoor Localisation

Robust Indoor Localization in a Reverberant Environment Using Microphone Pairs and Asynchronous Acoustic Beacons

Study on passive location method of shallow water acoustic source with single hydrophone

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