BLT*: Informed Belief Localization Trees for Uncertainty-Aware Planning on Digital Twins

2026-10-01

AI Summary

This study addresses the challenge of uncertainty-aware planning based on point cloud observations in large-scale outdoor digital twins by proposing the Informed BLT* algorithm. This method extends RRT* into belief space and introduces the Wasserstein metric to enable efficient node connectivity and information reuse, thereby avoiding redundant observation propagation. By integrating a Gaussian belief model with sampling-based planning, it establishes a framework for generating semantic digital twins. Experimental results demonstrate that the proposed algorithm significantly accelerates initial solution discovery while achieving competitive cost convergence, offering an efficient solution for uncertainty-aware planning in complex environments.

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.

Problem

• Belief Space Planning
• Digital Twins
• Uncertainty-Aware Planning
• Point-Cloud Observations

Innovation

• Belief Space Planning
• Digital Twins
• 2-Wasserstein Metric
• Informed RRT*
• Point-Cloud Localization