Deep Learning Based Stair Detection and Statistical Image Filtering for Autonomous Stair Climbing

Abstract

Mobile robots are widely used in the surveillance industry, for military and industrial applications. To carry out surveillance tasks like urban search and rescue operation, the ability to traverse stairs is of immense significance. This paper presents a deep learning based approach for stair detection, statistical filtering on images for the estimation of stair alignment, and novel mechanical design for an autonomous stair climbing robot. The primary objective is to solve the problem of indoor locomotion over staircases with the proposed implementation. The detection of stairs in an image is a traditional problem, and the most recent approaches are centered around hand-crafted texture-based Gabor filters. However, with the advent of deep learning methods, we could arrive at more scalable and robust detection schemes. The proposed statistical filtering eliminates the need for manual tuning of parameters of the edge detector and the Hough accumulator. The experimental results of stair detection and stair alignment algorithm are demonstrated in this paper.