Localization in highly dynamic environments using dual-timescale NDT-MCL

Conference Article


IEEE International Conference on Robotics and Automation (ICRA)





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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.



Scientific reference

R. Valencia, J. Saarinen, H. Andreasson, J. Vallvé, J. Andrade-Cetto and A. Llilienthal. Localization in highly dynamic environments using dual-timescale NDT-MCL, 2014 IEEE International Conference on Robotics and Automation, 2014, Hong Kong, pp. 3956-3962.