Trajectory Segmentation

Trajectory segmentation involves dividing continuous movement data into meaningful sub-sequences, a crucial step in various applications like robotics and transportation analysis. Current research focuses on developing robust and efficient segmentation methods, employing techniques such as deep learning architectures (e.g., autoencoders and RNNs) and novel trajectory representations (e.g., screw-based representations incorporating both translation and rotation). These advancements aim to improve the accuracy and efficiency of tasks ranging from robotic motion planning and control to transportation mode identification and simultaneous localization and mapping (SLAM), ultimately leading to more adaptable and reliable systems.