Bayesian programming for topological global localization with fingerprints

Adriana Tapus; Stefan Heinzer; Roland Siegwart

doi:10.1109/robot.2004.1307214

Bayesian programming for topological global localization with fingerprints

Adriana Tapus, Stefan Heinzer, Roland Siegwart

ENAC-IIC-GEL

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper presents a localization algorithm for indoor environments. The environmental model is topological and the approach describes how a multimodal perception increases the reliability for the topological localization problem for mobile robots, by using the Bayesian Programming formalism. For the topological framework the fingerprint concept is used. This type of representation permits a reliable and distinctive environment modeling. Experimental results of a mobile robot equipped with a multi sensor system composed of two 180° laser range finders and an omni-directional camera are reported.

Original language	English
Pages (from-to)	598-603
Number of pages	6
Journal	Proceedings - IEEE International Conference on Robotics and Automation
Volume	2004
Issue number	1
DOIs	https://doi.org/10.1109/robot.2004.1307214
Publication status	Published - 1 Jan 2004
Externally published	Yes
Event	Proceedings- 2004 IEEE International Conference on Robotics and Automation - New Orleans, LA, United States Duration: 26 Apr 2004 → 1 May 2004

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10.1109/robot.2004.1307214

Cite this

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title = "Bayesian programming for topological global localization with fingerprints",

abstract = "This paper presents a localization algorithm for indoor environments. The environmental model is topological and the approach describes how a multimodal perception increases the reliability for the topological localization problem for mobile robots, by using the Bayesian Programming formalism. For the topological framework the fingerprint concept is used. This type of representation permits a reliable and distinctive environment modeling. Experimental results of a mobile robot equipped with a multi sensor system composed of two 180° laser range finders and an omni-directional camera are reported.",

author = "Adriana Tapus and Stefan Heinzer and Roland Siegwart",

year = "2004",

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doi = "10.1109/robot.2004.1307214",

language = "English",

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AU - Tapus, Adriana

AU - Heinzer, Stefan

AU - Siegwart, Roland

PY - 2004/1/1

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N2 - This paper presents a localization algorithm for indoor environments. The environmental model is topological and the approach describes how a multimodal perception increases the reliability for the topological localization problem for mobile robots, by using the Bayesian Programming formalism. For the topological framework the fingerprint concept is used. This type of representation permits a reliable and distinctive environment modeling. Experimental results of a mobile robot equipped with a multi sensor system composed of two 180° laser range finders and an omni-directional camera are reported.

AB - This paper presents a localization algorithm for indoor environments. The environmental model is topological and the approach describes how a multimodal perception increases the reliability for the topological localization problem for mobile robots, by using the Bayesian Programming formalism. For the topological framework the fingerprint concept is used. This type of representation permits a reliable and distinctive environment modeling. Experimental results of a mobile robot equipped with a multi sensor system composed of two 180° laser range finders and an omni-directional camera are reported.

U2 - 10.1109/robot.2004.1307214

DO - 10.1109/robot.2004.1307214

M3 - Conference article

AN - SCOPUS:3042619123

SN - 1050-4729

VL - 2004

SP - 598

EP - 603

JO - Proceedings - IEEE International Conference on Robotics and Automation

JF - Proceedings - IEEE International Conference on Robotics and Automation

IS - 1

T2 - Proceedings- 2004 IEEE International Conference on Robotics and Automation

Y2 - 26 April 2004 through 1 May 2004

ER -

Bayesian programming for topological global localization with fingerprints

Abstract

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