Robust Odometry

Robust odometry aims to accurately estimate the movement of a robot or vehicle, even in challenging environments with sensor limitations or degraded data. Current research focuses on tightly-coupled sensor fusion, integrating data from diverse sources like LiDAR, radar, IMUs, and wheel or leg odometry, often using optimization techniques such as factor graphs or Kalman filters to achieve complementary drift correction and online calibration. These advancements are crucial for reliable autonomous navigation in various applications, including robotics, autonomous driving, and surveying, enabling more robust and accurate localization in scenarios previously considered difficult or impossible.