Ego Motion Estimation

Ego-motion estimation aims to determine a system's movement (position and orientation) over time, crucial for autonomous navigation and 3D scene reconstruction. Current research emphasizes improving accuracy and robustness, particularly in challenging conditions like low light, dynamic environments, and noisy sensor data, often employing deep learning models (e.g., convolutional neural networks, transformers) and integrating data from multiple sensor modalities (e.g., cameras, IMUs, radar, LiDAR). These advancements are significant for robotics, autonomous driving, and augmented/virtual reality applications, enabling more reliable and precise localization and mapping capabilities.