Localization in highly dynamic environments using dual-timescale NDT-MCL
Tipus de documentText en actes de congrés
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
Projecte de la Comissió EuropeaCARGO-ANTS - Cargo handling by Automated Next generation Transportation Systems for ports and terminals (EC-FP7-605598)
SPENCER - Social situation-aware perception and action for cognitive robots (EC-FP7-600877)
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.
CitacióValencia, R. [et al.]. Localization in highly dynamic environments using dual-timescale NDT-MCL. A: IEEE International Conference on Robotics and Automation. "2014 IEEE International Conference on Robotics and Automation (ICRA)". Hong Kong: Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 3956-3962.
Versió de l'editorhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6907433