Odometry Dataset

Odometry datasets are crucial for developing and evaluating algorithms that estimate the movement of robots and other mobile platforms, a fundamental task in robotics and autonomous systems. Current research focuses on creating diverse datasets capturing various environments (indoor/outdoor, underground), sensor modalities (LiDAR, cameras, IMUs, radar), and platform types (ground vehicles, drones, pedestrians), often addressing challenges like limited field-of-view and ill-conditioned data. Advanced deep learning architectures, including transformers and attention mechanisms, are increasingly used to process these datasets and improve odometry accuracy and robustness. These datasets and associated algorithms are essential for advancing autonomous navigation, particularly in GPS-denied environments, and for improving the safety and reliability of robotic systems.