Autonomous transportation will lead to major benefits in safety, economy and ecology. Although the associated technology has been an active field of research in the last decades, some problems have not been fully solved yet. Robust and efficient localization is a key component especially in urban scenarios. This thesis deals with the design and development of a system for landmark-based localization in urban scenarios suitable for autonomous driving. The sensor input is limited to a stereo camera pair, vehicle odometry and an off-the-shelf GPS. Prior knowledge in the form of a landmark map is also available.
Pole-like structures are identified as robust, long-term stable and common three-dimensional landmarks in urban scenarios. These are easily detectable by a stereo camera and are used as primary landmarks. In comparison to lane markers they have a lower occlusion probability and lower change rate. As pole-like structures can be rather small, a high quality depth reconstruction is crucial for robust detection. Several contributions are made in the field of automotive stereo vision, targeting long-term stability, robustness and efficiency. A new matching cost is presented and Semi-Global Matching is modified to become more reliable and more scalable. A robust extraction method for pole-like landmarks is introduced. The localization method proposed uses particle filters and the complete processing chain from feature extraction to processing a latency corrected vehicle pose output is covered. Field tests with an autonomous vehicle in urban environments and accuracy measures derived from real-driving data demonstrate the performance of the approach.