Feature-based mapping and self-localization for road vehicles using a single grayscale camera

This paper introduces a precise self-localization method for road vehicles. The presented approach is based on a single grayscale camera in addition with a conventional estimation of the ego motion and a map of the environment. This map is built in advance and independently from the localization process utilizing the same techniques. The proposed algorithm is based on Maximally Stable Extremal Regions which are robust features that are extracted from grayscale images. These features are matched in consecutive images using moment invariants. Together with an estimation of the ego motion, a 3D reconstruction of corresponding landmarks is obtained by applying multiple view geometry. For the unsupervised mapping process, landmarks are tracked and their corresponding global coordinates are stored in a geospatial database using a high-precision real-time kinematic system. The localization process itself is based on a particle filter to estimate the pose of the vehicle by making use of the previously generated map and currently observed landmarks. A standard GPS receiver is used to initialize the pose estimate. The evaluation with real world data shows that this approach achieves very good results despite the marginal sensor setup.