Comparison of different approaches to visual terrain classification for outdoor mobile robots

In this paper, we present a comparison of multiple approaches to visual terrain classification for outdoor mobile robots based on different color, texture and local features. We introduce and compare three novel composite descriptors called CEDD, FCTH and JCD, with traditional color and texture descriptors, such as LTP, SCD, EHD and a descriptor called CSD-HTD generated by late fusion method. We also test three BOW models based on SIFT, SURF and ORB, respectively. We used two terrain classification datasets of which the images were captured from outdoor moving robots under different weather and ground conditions. Hence some of the images are blurred or unideally exposed. We utilize ELM, SVM and NN for classification to evaluate the performance of different combinations of image descriptors and classifiers. Experiments demonstrate that JCD can represent different terrain images with significant inter-class discrepancies, and ELM has mild optimization constraints and obtains better generalization performance. Results show that the approach based on JCD descriptor and ELM classifier performs best in term of classification effectiveness and it is suitable for real-time outdoor visual terrain classification.