Terrain perception for robot navigation

摘要

This paper presents a method to forecast terrain trafficability from visual appearance. During training, the system identifies a set of image chips (or exemplars) that span the range of terrain appearance. Each chip is assigned a vector tag of vehicle-terrain interaction characteristics that are obtained from simple performance models and on-board sensors, as the vehicle traverses the terrain. The system uses the exemplars to segment images into regions, based on visual similarity to the terrain patches observed during training, and assigns the appropriate vehicle-terrain interaction tag to them. This methodology will therefore allow the online forecasting of vehicle performance on upcoming terrain. Currently, the system uses a fuzzy c-means clustering algorithm for training. In this paper, we explore a number of different features for characterizing the visual appearance of the terrain and measure their effect on the prediction of vehicle performance.