Combining multiple sensor modalities for a localisation robust to smoke

Brunner,; Peynot,; Vidal-Calleja, Teresa

doi:10.1109/iros.2011.6048181

Cited by 3 publications

(2 citation statements)

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“…Such a combination is regularly used in modern systems (cf. [15, 17, 20]) and therefore gives a more realistic scenario for the evaluation of the influence of the environment on the positioning accuracy of state-of-the-art systems. The next subsections will elaborate the building blocks of this combined approach and describe how they interact with each other.…”

Section: Test Systems For the Case Study In (Non)industrial Environmentsmentioning

confidence: 99%

Localization in an Industrial Environment: A Case Study on the Difficulties for Positioning in a Harsh Environment

Hölzl

Neumeier

Ostermayer

2015

International Journal of Distributed Sensor Networks

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While indoor localization has become a highly growing application domain in the last few years, it is hardly investigated in industrial environments. Interferences of magnetic fields, shadowing, and multipath propagation discourage positioning system vendors from porting their techniques to these harsh environments. However, the actual influence of these interferences on the positioning accuracy and the differences between an industrial and a nonindustrial environment have never been evaluated. This paper analyzes the actual differences for a positioning technique that is based on Wi-Fi fingerprinting, map matching, dead reckoning, filtering, and a plausibility determination. An investigation of the Wi-Fi signal strengths and compass sensor values in an industrial and a nonindustrial environment thereby showed that the differences between them are significant. In fact, it shows that more interferences and shadowing in the industrial environment resulted in even more accurate positioning.

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Section: Test Systems For the Case Study In (Non)industrial Environmentsmentioning

confidence: 99%

Localization in an Industrial Environment: A Case Study on the Difficulties for Positioning in a Harsh Environment

Hölzl

Neumeier

Ostermayer

2015

International Journal of Distributed Sensor Networks

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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%

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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Robotic technologies for first response

Adams¹

2013

Handbook of Emergency Response

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Combining multiple sensor modalities for a localisation robust to smoke

Cited by 3 publications

References 0 publications

Localization in an Industrial Environment: A Case Study on the Difficulties for Positioning in a Harsh Environment

Localization in an Industrial Environment: A Case Study on the Difficulties for Positioning in a Harsh Environment

EchoSLAM: Simultaneous localization and mapping with acoustic echoes

Robotic technologies for first response

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