A robust negative obstacle detection method using seed-growing and dynamic programming for visually-impaired/blind persons

Though a significant amount of work has been done on detecting obstacles, not much attention has been given to the detection of drop offs, e.g., sidewalk curbs, downward stairs, and other hazards. In this paper, we propose algorithms for detecting negative obstacles in an urban setting using stereo vision and two-stage dynamic programming (TSDP) technique. We are developing computer vision algorithms for sensing important terrain features as an aid to blind navigation, which interpret visual information obtained from images collected by cameras mounted on camera legs nearly as high as young person. This paper focuses specifically on a novel computer vision algorithm for detecting negative obstacles (i.e. anything below the level of the ground, such as holes and drop-offs), which are important and ubiquitous features on and near sidewalks and other walkways. The proposed algorithm is compared to other algorithms such as belief propagation and random growing correspondence seeds (GCS). According to the results, the proposed method achieves higher speed, more accurate disparity map and lower RMS errors. The speed of the proposed algorithm is about 28% higher than the random GCS algorithm. We demonstrate experimental results on typical sidewalk scenes to show the effectiveness of the proposed method.