Image Understanding for Robot Navigation

Abstract

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 and measures terrain trafficability characteristics as the vehicle traverses the terrain. Each chip is assigned a vector tag representing the measured vehicle-terrain interaction properties. After training, the system uses the exemplars to segment images into regions, based on visual similarity to terrain patches observed during training, and assigns the appropriate vehicle-terrain interaction tag to them. The system will therefore allow the online forecasting of vehicle performance on upcoming terrain.