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Localization in highly dynamic environments using dual-timescale NDT-MCL
Valencia Carreño, Rafael; Saarinen, Jari; Andreasson, Henrik; Vallvé Navarro, Joan; Andrade-Cetto, Juan; Lilienthal, Achim
Universitat Politècnica de Catalunya. Institut de Robòtica i Informàtica Industrial; Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel.ligents
Industrial environments are rarely static and often their configuration is continuously changing due to the material transfer flow. This is a major challenge for infrastructure free localization systems. In this paper we address this challenge by introducing a localization approach that uses a dual- timescale approach. The proposed approach - Dual-Timescale Normal Distributions Transform Monte Carlo Localization (DT- NDT-MCL) - is a particle filter based localization method, which simultaneously keeps track of the pose using an apriori known static map and a short-term map. The short-term map is continuously updated and uses Normal Distributions Transform Occupancy maps to maintain the current state of the environment. A key novelty of this approach is that it does not have to select an entire timescale map but rather use the best timescale locally. The approach has real-time performance and is evaluated using three datasets with increasing levels of dynamics. We compare our approach against previously pro- posed NDT-MCL and commonly used SLAM algorithms and show that DT-NDT-MCL outperforms competing algorithms with regards to accuracy in all three test cases.
Àrees temàtiques de la UPC::Informàtica::Robòtica
robots
Classificació INSPEC::Automation::Robots
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
info:eu-repo/semantics/submittedVersion
info:eu-repo/semantics/conferenceObject
Institute of Electrical and Electronics Engineers (IEEE)
         

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