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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

© Robotics and Autonomous Systems, 2013, vol. 61, núm. 12, p. 1539-1558

Elsevier

Author: Bacca Cortés, Eval Bladimir
Salvi, Joaquim
Cufí i Solé, Xavier
Date: 2013
Abstract: 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
ISSN: 0921-8890
Document access: http://hdl.handle.net/10256/11542
Language: eng
Publisher: Elsevier
Collection: MICINN/PN 2012-2014/CTM2011-29691-C02-02
AGAUR/2009-2014/2009 SGR-380
Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.robot.2013.07.003
Articles publicats (D-ATC)
Is part of: © Robotics and Autonomous Systems, 2013, vol. 61, núm. 12, p. 1539-1558
Rights: Tots els drets reservats
Subject: Robòtica
Robotics
Robots -- Sistemes de control
Robots -- Control systems
Title: Long-term mapping and localization using feature stability histograms
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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