BLT*: Informed Belief Localization Trees for Uncertainty-Aware Planning on Digital Twins
Informed Belief Localization Trees (Informed BLT) is a sampling-based planning algorithm for large outdoor digital twins. It efficiently connects belief states using the 2-Wasserstein metric while incorporating probabilistic collision constraints and available information. This allows for steering and rewiring without repeatedly propagating observations, enabling real-world planning with point-cloud localization.