A New Terrain Recognition Approach for Predictive Control of Assistive Devices Using Depth Vision

Abstract

Vision based systems for terrain detection play important roles in mobile robotics, and recently such systems emerged for locomotion assistance of disabled people. For instance, they can be used as wearable devices to assist blind people or to guide prosthesis or exoskeleton controller to retrieve gait patterns being adapted to the executed task (overground walking, stairs, slopes, etc.). In this paper, we present a computer vision-based algorithm achieving the detection of flat ground, steps, and ramps using a depth camera. Starting from point cloud data collected by the camera, it classifies the environment as a function of extracted features. We further provide a pilot validation in an indoor environment containing a rich set of different types of terrains, even with partial occlusion, and observed that the overall system accuracy is above 94$\%$. The paper further shows that our system needs less computational resources than recently published concurrent approaches, owing to the original transformation method we developed.

This work was supported by the Conseil de l’action internationale of UCLouvain.