Surface Type Classification for Autonomous Robots Using Temporal, Statistical and Spectral Feature Extraction and Selection

Real-time surface recognition has become a crucial component in assuring the safe walking of intelligent autonomous robots in a complex human-living interior environment. Numerous studies have been done addressing the problem recently. Still, there is a scope of improvements for accurate classification and inference time. In this paper, we have extracted features from accelerometer and gyroscope data in the temporal, statistical and spectral domain and classified them using a tree-based ensembling classification algorithm. We have achieved 80.81% mean accuracy, classifying 9 different surfaces with 1.0% standard deviation in 10-fold cross-validation and 97.25% average AUC score. Our method acquired state-of-the-art accuracy ensuring minimal inference time which is essential for real-time recognition for the autonomous robots.