Dynamic Object

Dynamic object research focuses on understanding and representing the movement and behavior of objects within a scene, primarily aiming for robust detection, tracking, and reconstruction in real-time. Current efforts utilize diverse approaches, including LiDAR-inertial fusion, Kalman filtering for tracking, transformer-based architectures for object detection and classification, and novel neural representations like NeRFs for 3D reconstruction, often incorporating deep learning for improved accuracy and efficiency. This field is crucial for advancing robotics, autonomous navigation, and computer vision, enabling safer and more effective interaction with dynamic environments.