Multi-drive feature association for automated map generation using low-cost sensor data

In this paper, we present an approach targeting the automated road map generation for autonomously driving vehicles using low-cost GPS sensor data in a multi-drive setup. Multiple drives with deployed commodity smartphone and stereo camera system are recorded as input data. To overcome the high position uncertainties of the GPS sensor, the GPS trajectory is fused with ego-motion estimates of the vehicle computed by visual odometry. Landmarks are extracted from the recorded imagery data and fused over all recorded drives. The resulting road map consists of a simple, parametric representation of globally referenced lane markings with low storage impact. The challenging aspect in this work is the feature association between multiple drives. Different characteristics of dashed center lines are exploited for this purpose to handle the low precision of the sensor data. The resulting association information is the building block for graph-based SLAM to optimize vehicle poses and landmarks simultaneously. The approach is finally evaluated on real world data comparing the low-precision sensor data with high-precision sensor data as ground-truth.