Belief Roadmaps with Uncertain Landmark Evanescence

TL;DR

This paper introduces BRULE, an extension of Belief Roadmaps, that models landmark evanescence with Gaussian mixtures to improve robot navigation under uncertain and changing landmarks, demonstrated through simulations and real-world tests.

Contribution

We develop BRULE, a novel planning method that efficiently accounts for landmark disappearance over time using Gaussian mixtures, enhancing localization accuracy.

Findings

BRULE effectively models landmark evanescence during planning.

The approach maintains high-quality solutions with reduced computational complexity.

Experimental results show improved navigation performance in dynamic environments.

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.