Efficient stairway detection and modeling for autonomous robot climbing

Abstract

This paper presents an accurate and computationally efficient stairway detection algorithm, robust to measurement noise. While most similar methods depend heavily on detecting three-dimensional (3D) features, this work focuses on locating two-dimensional (2D) features embedded in 3D data. The main idea is: each stair is a sequence of lines with similar domain of definition, and the staircase is a sequence of such elements. To avoid unnecessary computations, an octree is used to down-sample the environment point-cloud data. The resulting centroids are stored in n arrays, each containing points with equal height. This effectively segments the 3D data into n 2D slices. The Hough transform is used to scan each array for lines. the remainder of the algorithm detects lines with the same domain of definition and determines if sequences that may correspond to stairways exist. If these sequences meet the required tolerances, then a staircase is detected. For each detected stairway a model that contains the width, height, depth, and amount of stairs is returned. The proposed algorithm is able to efficiently locate and model staircases on different poses and is robust to noise from the measured data, as indicated by the experimental results.