Map Representation

Map representation in robotics and autonomous driving focuses on creating efficient and informative models of the environment for tasks like navigation, planning, and localization. Current research emphasizes developing representations that integrate diverse data sources (e.g., LiDAR, cameras, standard definition maps) using techniques like Gaussian splatting, tri-plane hashing, and transformer-based encoders, often aiming for real-time performance and scalability. These advancements are crucial for improving the robustness and reliability of autonomous systems, particularly in complex and dynamic environments, and are driving progress in areas such as visual SLAM, motion planning, and scene understanding.