Multi-cue, Model-Based Detection and Mapping of Road Curb Features Using Stereo Vision

In this paper a new approach for road curb detection is presented, which is able to detect road curbs independently of their geometry and orientation in relation to the car. This approach includes a novel algorithm for curb feature detection, which combines cues for road curbs from a 3D point cloud and an intensity image, both gained from a stereo camera system. The edge detection operators used for the detection of curb features are adapted according to curb models. With these adaptive operators road curbs can be found independently from their position and orientation in the image and the correct curb height can be calculated. Further we introduce a computation efficient grid map, which is based on a local linear description of the road curb in every cell and is able to represent the road curbs three-dimensionally. A chaining mechanism, which benefits from the estimated curb directions in the grid map, generates road curbs represented as 3D polygonal chains. An evaluation of the detected curbs using DGPS measurements of the curbs and the car shows a high position and height accuracy for this real time capable approach.