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This research introduces Informed BLT*, a novel sampling-based planning algorithm that scales uncertainty-aware navigation to large outdoor digital twins using point-cloud observations. By adapting RRT* with the 2-Wasserstein metric, the method efficiently reuses measurement information to handle probabilistic collision constraints without repeated observation propagation. The approach demonstrates faster solution discovery and competitive cost convergence in simulated environments, advancing autonomous systems capable of operating reliably in complex, real-world settings.
BLT*: Informed Belief Localization Trees for Uncertainty-Aware Planning on Digital Twins
arXiv:2610.01972v1 Announce Type: new Abstract: We present Informed Belief Localization Trees* (Informed BLT*), a sampling-based belief space planning (BSP) algorithm that scales to large outdoor digital twins with point-cloud observations. We adapt RRT* and Informed RRT* to belief space using the $2$-Wasserstein ($W_2$) metric. Assuming isotropic Gaussian beliefs, sampled belief states can be connected efficiently while accounting for available information and probabilistic collision constraints. This enables steering and rewiring without repeatedly propagating observations, and allows previously computed measurement information to be reused. We present a framework to generate semantically labelled digital twins for planning in real-world environments with point-cloud-based localization. Experiments in simulated environments and digital twins show faster initial solution discovery in most maps with competitive cost convergence.
Abstract:We present Informed Belief Localization Trees* (Informed BLT*), a sampling-based belief space planning (BSP) algorithm that scales to large outdoor digital twins with point-cloud observations. We adapt RRT* and Informed RRT* to belief space using the $2$-Wasserstein ($W_2$) metric. Assuming isotropic Gaussian beliefs, sampled belief states can be connected efficiently while accounting for available information and probabilistic collision constraints. This enables steering and rewiring without repeatedly propagating observations, and allows previously computed measurement information to be reused. We present a framework to generate semantically labelled digital twins for planning in real-world environments with point-cloud-based localization. Experiments in simulated environments and digital twins show faster initial solution discovery in most maps with competitive cost convergence.
Robotics (cs.RO)
arXiv:2610.01972 [cs.RO]
(or arXiv:2610.01972v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.01972 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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