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Safe mobility in rough terrain is important for high-risk missions. In order to achieve stability and mobility precision it is necessary to have a good knowledge of the terrain properties. This work treats the problem of outdoor classification terrain analyzing proprioceptives sensor data, current measure, wheel speeds and slippage. The use of principal component analysis (PCA) reduces the space dimension of acquired data to a lower dimension to classificate in which terrain the robot is moving. This paper presents experimental results to validate the proposed methodology.

Terrain Classification for outdoor mobile robots using PCA