Example Concept: Terrain classification in robotics often involves using LIDAR, stereo cameras, or depth sensors to capture 3D data of the environment. This data is processed through algorithms that segment the terrain into categories based on features like elevation changes and surface roughness. Machine learning models can further refine this classification by learning from labeled datasets, allowing the robot to predict and adapt to different terrains in real-time.