Terrain Classification

Terrain classification aims to automatically identify and categorize different types of terrain using various sensor data, primarily to improve the autonomy and robustness of robots navigating challenging environments. Current research focuses on developing and refining machine learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and vision transformers (ViTs), often incorporating multimodal data fusion (e.g., combining visual and inertial data) and uncertainty quantification to enhance reliability. These advancements are crucial for applications ranging from planetary exploration rovers to legged robots operating in unstructured terrains, improving navigation efficiency, safety, and the overall success rate of autonomous missions.