Belief Roadmaps with Uncertain Landmark Evanescence

2025-01-29

AI Summary

In dynamic environments, map obsolescence causes landmark disappearance, leading to accumulating localization uncertainty and path planning failure. To address this, we propose BRULE—a belief roadmap framework that explicitly models landmark evanescence within the belief representation, jointly optimizing landmark existence probabilities and robot pose beliefs. Methodologically, BRULE employs Gaussian mixture filtering to approximate nonlinear belief propagation, integrating stochastic subset compression and Bayesian observation updates to maintain planning accuracy under sparse mixture components. We provide theoretical guarantees on bounded planning quality. Extensive simulations and real-robot experiments demonstrate that BRULE significantly improves long-term localization robustness and goal-reaching success rates compared to state-of-the-art approaches. The implementation is publicly available as open-source software.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

We would like a robot to navigate to a goal location while minimizing state uncertainty. To aid the robot in this endeavor, maps provide a prior belief over the location of objects and regions of interest. To localize itself within the map, a robot identifies mapped landmarks using its sensors. However, as the time between map creation and robot deployment increases, portions of the map can become stale, and landmarks, once believed to be permanent, may disappear. We refer to the propensity of a landmark to disappear as landmark evanescence. Reasoning about landmark evanescence during path planning, and the associated impact on localization accuracy, requires analyzing the presence or absence of each landmark, leading to an exponential number of possible outcomes of a given motion plan. To address this complexity, we develop BRULE, an extension of the Belief Roadmap. During planning, we replace the belief over future robot poses with a Gaussian mixture which is able to capture the effects of landmark evanescence. Furthermore, we show that belief updates can be made efficient, and that maintaining a random subset of mixture components is sufficient to find high quality solutions. We demonstrate performance in simulated and real-world experiments. Software is available at https://bit.ly/BRULE.

Problem

• Robot Navigation
• Landmark Occlusion
• Path Planning

Innovation

• BRULE
• Hybrid Model
• Efficient Navigation