Prediction of Vibrations as a Measure of Terrain Traversability in Outdoor Structured and Natural Environments

Abstract

Terrain recognition is an important task that a mobile robot has to accomplish autonomously to navigate in hazardous territories safely with no additional human monitoring. For this, sensory information should be employed to construct a good model to estimate the degree of traversability of upcoming terrains. In this paper, a regression-based method is proposed to estimate mobile robot vibration from terrain images as a description for terrain traversability. Texture attributes obtained from evaluation of the fractal dimension to describe the terrains were combined with appropriate acceleration features for function approximation using Gaussian Process regression GP. Results showed effectiveness of the method to predict motion data for different terrain configurations in structured and rough environments.