FIT-SLAM 2: Efficient 3D exploration with Fisher information and traversability-based adaptive roadmap

This paper presents FIT-SLAM 2, an enhanced framework for autonomous 3D exploration, integrating Fisher Information and a traversability-aware adaptive roadmap. Building on FIT-SLAM, our approach introduces frontier classification into local and global categories, a scheduling strategy for exploration path computation, and optimized real-time Fisher Information computation using pre-computed lookup tables to assess localization confidence and ensure safe exploration. FIT-SLAM 2 seamlessly integrates with the SLAM backend while iteratively constructing and updating an adaptive roadmap that optimizes both navigation efficiency and safety. This enables the robot to efficiently explore complex environments – including rocky terrains, caves, and mazes – while maintaining robust localization. Extensive experiments demonstrate that FIT-SLAM 2 achieves a 33% increase in exploration rate in unstructured environments along with a notable improvement in localization accuracy and computational efficiency over state-of-the-art methods. For reproducibility and future enhancements, we release our implementation at https://github.com/suchetanrs/FIT-SLAM.