Robust Visual Terrain Classification with Recurrent Neural Networks

Abstract

A novel approach for robust visual terrain classification by generating feature sequences on repeatedly mutated image patches is pre- sented. These sequences providing the feature vector progress under a cer- tain image operation are learned with Recurrent Neural Networks (RNNs). The approach is studied for image patch based terrain classification for wheeled robots. Thereby, various RNN architectures, namely, standard RNNs, Long Short Term Memory networks (LSTMs), Dynamic Cortex Memory networks (DCMs) as well as bidirectional variants of the men- tioned architecture are investigated and compared to recently used state- of-the-art methods for real-time terrain classification. The results show that the presented approach outperforms previous methods significantly.