Grid-based visual terrain classification for outdoor robots using local features

In this paper we present a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on local features. We compare the more traditional texture classification approaches, such as Local Binary Patterns, Local Ternary Patterns and a newer extension Local Adaptive Ternary Patterns, and also modify and test three non-traditional approaches called SURF, DAISY and CCH. We drove our robot under different weather and ground conditions and captured images of five different terrain types for our experiments. We did not filter out blurred images which are due to robot motion and other artifacts caused by rain, etc.We used Random Forests for classification, and cross-validation for the verification of our results. The results show that most of the approaches work well for terrain classification in a fast moving mobile robot, despite image blur and other artifacts induced due to extremely variant weather conditions.

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