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Long-term mapping and localization using feature stability histograms

This work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. FSH is built using a voting schema, where re-observed features are promoted; otherwise the feature progressively decreases its corresponding FSH value. FSH is inspired by the human memory model. This model introduces concepts of Short-Term Memory (STM), which retains information long enough to use it, and Long-Term Memory (LTM), which retains information for longer periods of time. If the entries in STM are continuously rehearsed, they become part of LTM. However, this work proposes a change in the pipeline of this model, allowing any feature to be part of STM or LTM depending on the feature strength. FSH stores the stability values of local features, stable features are only used for localization and mapping. Experimental validation of the FSH model was conducted using the FastSLAM framework and a long-term dataset collected during a period of one year at different environmental conditions. The experiments carried out include qualitative and quantitative results such as: filtering out dynamic objects, increasing map accuracy, scalability, and reducing the data association effort in long-term run

This work has been partially supported by the project RAIMON—Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref. CTM2011-29691-C02-02) funded by the Spanish Ministry of Science and Innovation, the LASPAUCOLCIENCIAS grant 136-2008, the University of Valle contract 644-19-04-95, and the consolidated research group’s grant SGR2009-00380

Elsevier

Director: Ministerio de Ciencia e Innovación (Espanya)
Generalitat de Catalunya. Agència de Gestió d’Ajuts Universitaris i de Recerca
Autor: Bacca Cortés, Eval Bladimir
Salvi, Joaquim
Cufí i Solé, Xavier
Data: 2013
Resum: This work proposes a system for long-term mapping and localization based on the Feature Stability Histogram (FSH) model which is an innovative feature management approach able to cope with changing environments. FSH is built using a voting schema, where re-observed features are promoted; otherwise the feature progressively decreases its corresponding FSH value. FSH is inspired by the human memory model. This model introduces concepts of Short-Term Memory (STM), which retains information long enough to use it, and Long-Term Memory (LTM), which retains information for longer periods of time. If the entries in STM are continuously rehearsed, they become part of LTM. However, this work proposes a change in the pipeline of this model, allowing any feature to be part of STM or LTM depending on the feature strength. FSH stores the stability values of local features, stable features are only used for localization and mapping. Experimental validation of the FSH model was conducted using the FastSLAM framework and a long-term dataset collected during a period of one year at different environmental conditions. The experiments carried out include qualitative and quantitative results such as: filtering out dynamic objects, increasing map accuracy, scalability, and reducing the data association effort in long-term run
This work has been partially supported by the project RAIMON—Autonomous Underwater Robot for Marine Fish Farms Inspection and Monitoring (Ref. CTM2011-29691-C02-02) funded by the Spanish Ministry of Science and Innovation, the LASPAUCOLCIENCIAS grant 136-2008, the University of Valle contract 644-19-04-95, and the consolidated research group’s grant SGR2009-00380
Format: application/pdf
Accés al document: http://hdl.handle.net/10256/11542
Llenguatge: eng
Editor: Elsevier
Col·lecció: info:eu-repo/semantics/altIdentifier/doi/10.1016/j.robot.2013.07.003
info:eu-repo/semantics/altIdentifier/issn/0921-8890
info:eu-repo/grantAgreement/MICINN//CTM2011-29691-C02-02/ES/ROBOT AUTONOMO SUBMARINO PARA LA INSPECCION Y MONITORIZACION DE EXPLOTACIONES DE ACUICULTURA MARINA/
AGAUR/2009-2014/2009 SGR-380
Drets: Tots els drets reservats
Matèria: Robòtica
Robotics
Robots -- Sistemes de control
Robots -- Control systems
Títol: Long-term mapping and localization using feature stability histograms
Tipus: info:eu-repo/semantics/article
Repositori: DUGiDocs

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