Unstructured Road SLAM using Map Predictive Road Tracking

In this paper, we present a simultaneous localization and mapping framework that combines filter-based road course tracking and GraphSLAM for localization and mapping in unstructured and rural areas. Road perception plays a crucial role, especially in areas without road markings, precise point positioning or HD-maps. In order to improve vehicle localization, we detect and track the road course. The road is modeled with a novel B-Spline measurement model and tracked with an Unscented Kalman Filter. LiDAR measurements from a Velodyne sensor are preprocessed, clustered and used as input. In addition, the road network from OpenStreetMap (OSM) is utilized to improve robustness in road detection and tracking. To correct a global vehicle position offset, we estimate the transformation between the tracked road course and the road network from OSM with an iterative closest point algorithm. For road course mapping as well as offset correction and smoothing of the vehicle trajectory, all information is accumulated and fused using GraphSLAM. Evaluation of our method is performed by comparing real data, collected with an experimental vehicle in an unstructured and urban area. Localization results are compared to a high-precision RTK INS/GNSS system. The results show that our method is able to capture the road robustly and to improve the global vehicle position under challenging environmental conditions.