Towards environment perceptionfor walking-aid robots:an improved staircase shape feature extraction method
Abstract
This paper introduces an innovative staircase shape feature extraction method for walking-aid robots to enhance en-vironmental perception and navigation. We present a robust method for accurate feature extraction of staircasesunder various conditions, including restricted viewpoints and dynamic movement. Utilizing depth camera-mountedrobots, we transform three-dimensional (3D) environmental point cloud into two-dimensional (2D) representations,focusing on identifying both convex and concave corners. Our approach integrates the Random Sample Consensusalgorithm with K-Nearest Neighbors (KNN)-augmented Iterative Closest Point (ICP) for efficient point cloud regis-tration. The results show an improvement in trajectory accuracy, with errors within the centimeter range. This workovercomes the limitations of previous approaches and is of great significance for improving the navigation and safetyof walking assistive robots, providing new possibilities for enhancing the autonomy and mobility of individuals withphysical disabilities.
