Mobile Object

Mobile object research focuses on understanding and interacting with moving objects in various contexts, primarily aiming for robust detection, tracking, and manipulation. Current efforts concentrate on developing efficient algorithms, such as graph-based methods and reinforcement learning, for tasks like object localization, mapping, and path planning in dynamic environments, often leveraging multimodal sensor data (e.g., LiDAR, cameras). These advancements have significant implications for autonomous driving, robotics, and logistics, enabling improved efficiency and safety in applications ranging from self-driving vehicles to automated delivery systems.