Inertial Simultaneous Localization and Mapping

Inertial Simultaneous Localization and Mapping (SLAM) aims to enable robots and autonomous systems to build maps of their surroundings while simultaneously tracking their location within those maps, using inertial sensors (like IMUs) in conjunction with other sensors (cameras, LiDAR, GPS). Current research emphasizes improving robustness and efficiency through various approaches, including tightly-coupled sensor fusion (e.g., integrating visual, inertial, and GPS data), graph-based optimization techniques, and deep learning methods for both front-end feature extraction and back-end map optimization. These advancements are crucial for enhancing the reliability and scalability of autonomous navigation in diverse environments, from agricultural fields to underground mines, impacting fields like robotics, autonomous driving, and augmented reality.