Localization in highly dynamic environments using dual-timescale NDT-MCL
2014 (English)In: 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE Robotics and Automation Society, 2014, 3956-3962 p.Conference paper (Refereed)
Industrial environments are rarely static and oftentheir configuration is continuously changing due to the materialtransfer flow. This is a major challenge for infrastructure freelocalization systems. In this paper we address this challengeby introducing a localization approach that uses a dualtimescaleapproach. The proposed approach - Dual-TimescaleNormal Distributions Transform Monte Carlo Localization (DTNDT-MCL) - is a particle filter based localization method,which simultaneously keeps track of the pose using an aprioriknown static map and a short-term map. The short-termmap is continuously updated and uses Normal DistributionsTransform Occupancy maps to maintain the current state ofthe environment. A key novelty of this approach is that it doesnot have to select an entire timescale map but rather use thebest timescale locally. The approach has real-time performanceand is evaluated using three datasets with increasing levels ofdynamics. We compare our approach against previously proposedNDT-MCL and commonly used SLAM algorithms andshow that DT-NDT-MCL outperforms competing algorithmswith regards to accuracy in all three test cases.
Place, publisher, year, edition, pages
IEEE Robotics and Automation Society, 2014. 3956-3962 p.
, Proceedings - IEEE International Conference on Robotics and Automation, ISSN 1050-4729
Localization, Monte Carlo Localization, Intra Logistics, Mapping
Research subject Computer Science
IdentifiersURN: urn:nbn:se:oru:diva-41234DOI: 10.1109/ICRA.2014.6907433ScopusID: 2-s2.0-84929180176OAI: oai:DiVA.org:oru-41234DiVA: diva2:780074
IEEE International Conference on Robotics and Automation (ICRA), Hongkong, China, May 31 - June 7, 2014
FunderEU, FP7, Seventh Framework Programme