Ground Plane

Ground plane estimation and utilization are crucial for various applications, particularly in robotics and autonomous driving, aiming to accurately identify and leverage the ground surface within a scene. Current research focuses on developing robust algorithms, often incorporating LiDAR and camera data, to estimate ground plane parameters even in challenging conditions like reflective surfaces or dynamic environments; techniques range from Kalman filtering to deep learning-based approaches that integrate ground plane information into tasks like object detection and depth estimation. Accurate ground plane estimation significantly improves the performance and reliability of autonomous systems by providing essential geometric constraints and context for scene understanding and navigation.