Semi-Markov Process Based Localization Using Radar in Dynamic Environments

Automotive localization in urban environment faces natural long-term changes of the surroundings. In this work, a robust Monte-Carlo based localization is presented. Robustness is achieved through a stochastic analysis of previous observations of the area of interest. The model uses a grid-based Markov chain to instantly model changes. An extension of this model by a Lévy process allows statements about reliability and prediction for each cell of the grid. Experiments with a vehicle equipped with four short range radars show the localization accuracy performance improvement in a dynamic environment.