Plane-based detection of staircases using inverse depth

Staircases are a common feature in urban environments. Nevertheless they often pose a navigational challenge to both visually impaired people and autonomous mobile systems. In this paper, we propose a plane-based approach to the problem of staircase detection using depth data in inverse depth coordinates. This forms a basis for our long term goal of assisting visually impaired people in navigating indoor environments. Our proposed algorithm iteratively uses Preemptive RANSAC in a segment-thenfit approach to detect steps of a staircase. This allows our algorithm to detect the presence of a staircase, identify its inclination, and model each step of the staircase as a plane model in 3D space. Experiments were conducted using a real world dataset of 121 images with manually labelled ground truth. Results shows a Type I and Type II error rate of approximately 1 and 5% respectively for the detection of staircases. Our algorithm runs at approximately 16 frames per second.