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.
Image Understanding for Robot Navigation
2006
9 pages
Report
No indication
English
Navigation Systems , Human Factors Engineering , Meteorological Data Collection, Analysis, & Weather , Electron Tubes , Navigation , Visual signals , Trafficability , Forecasting , Robots , Online systems , Images , Terrain , Chips(Electronics) , Vehicles , Ground vehicles , Performance(Engineering)
Autonomous Navigation Robot Based on Real-Time Image Processing
Springer Verlag | 2017
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