Low-Obstacle Detection Using Stereo Vision

Real-time obstacle detection is a key component of autonomous vehicles. In this context, low obstacles are particularly challenging, as they are often discarded by traditional algorithms. Curb detection methods that can potentially be suitable for the problem usually target roads with clearly defined curbs and sidewalks. We propose a real-time algorithm for the detection of low obstacles (including, but not restricted to curbs), merging 2-D and 3-D information from stereo imaging. A set of candidate object lines is extracted based on their combined 2-D and 3-D features, tracked over time and clustered according to a novel similarity metric. Finally, a 3rd order polynomial spline is fitted to each cluster to represent the obstacle. The proposed system can deal with noisy and incomplete point clouds and keeps the model assumptions to a minimum. To evaluate the algorithm, a new stereo dataset is provided and made available online. We present experiments in different scenarios and lighting conditions, illustrating the applicability of the method.