Ego Motion

Ego motion, the estimation and understanding of a system's own movement, is a crucial area of research in robotics and computer vision, aiming to enable accurate self-localization and scene understanding. Current research focuses on developing robust and efficient algorithms for ego-motion estimation using diverse sensor modalities (cameras, LiDAR, radar, IMUs), often incorporating techniques like deep learning (e.g., transformers, convolutional neural networks), spiking neural networks, and iterative closest point (ICP) algorithms. These advancements are significantly impacting fields like autonomous navigation, augmented reality, and assistive technologies by improving the accuracy and reliability of perception systems in dynamic environments.