Lane-level positioning with sparse visual cues

Vehicle localization and autonomous navigation consist of accurately positioning a vehicle in a lane. This paper presents topological localization methods by matching the visual cues from the on-board monocular camera images and the preprocessed database. We propose two methods for vehicle localization. The first exploits the 3D information of the sparse visual cues from the database. The relative vehicle rotation and translation are extracted using SOREPP method which is able to handle challenging scenarios with extremely low inlier fractions. The translation length is selected among multiple triangulation candidates. In order to select the best candidate, we suggest a robust soft-threshold estimation method which is not prone to local maxima even when the inliers' fragment is very small. The other method seeks for a refined test vehicle position estimation using a soft-threshold on Sampson distances and Cross-ratio measurements given a current noisy vehicle pose and several near-by database images. This method does not require any 3D knowledge for operation. The main challenge our optimization algorithm addresses is due to the camera and scene configurations. The database images and the test image are all taken from positions which are approximately co-linear. In addition, the scene points visible in all these images are almost co-linear with the camera positions. In this configuration, standard localization algorithms will exhibit difficulties in obtaining accurate results. The novel algorithm we present here is able to overcome this problem. The suggested localization methods are initialized by a rough position estimation thus require a regular vehicle GPS on-board. A monocular camera is required for the topological localization. We evaluate the proposed methods on real data from the KITTI database with the RTK-GPS output as ground truth.