Abstract-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 dualtimescale 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 proposed 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.
To handle the highly dynamic changes in an industrial environment, a dual-timescale normal distributions transform Monte Carlo localization (NDT-MCL) algorithm is proposed in REF , which simultaneously keeps track of the pose using a prior static map and a short-term current map; the position was decided by matching the current scan to the best timescale map.
Localization in highly dynamic environments using dual-timescale NDT-MCL
2014 IEEE International Conference on Robotics and Automation (ICRA)
2014 IEEE International Conference on Robotics and Automation (ICRA)
Computer Science Engineering