A Monte Carlo particle filter formulation for mapless-based localization

In this paper, we extend the Monte Carlo Localization formulation for a more efficient global localization using coarse digital maps (for instance, the OpenStreetMap maps). The proposed formulation uses the map constraints in order to reduce the state dimension, which is ideal for a Monte Carlo-based particle filter. Also, we propose including to the data association process the matching of the traffic signals’ information to the road properties, so that their exact position do not need to be previously mapped for updating the filter. In the proposed approach, no low-level point cloud mapping was required and neither the use of LIDAR data. The experiments were conducted using a dataset collected by the CARINA II intelligent vehicle and the results suggest that the method is adequate for a localization pipeline. The dataset is available online and the code is available on GitHub.