Efficient Belief Space Planning in High-Dimensional State Spaces using PIVOT: Predictive Incremental Variable Ordering Tactic
Khen Elimelech, Vadim Indelman

TL;DR
This paper introduces PIVOT, a method for reordering variables in belief space planning to reduce computational complexity in high-dimensional, uncertain environments, demonstrated effectively in active SLAM tasks.
Contribution
The paper proposes PIVOT, a novel variable reordering tactic that minimizes affected variables during planning, improving efficiency without sacrificing accuracy in belief space planning.
Findings
Significantly reduced computation time in active SLAM simulations
Effective in high-dimensional belief space planning scenarios
Applicable to general distributions without accuracy loss
Abstract
In this work, we examine the problem of online decision making under uncertainty, which we formulate as planning in the belief space. Maintaining beliefs (i.e., distributions) over high-dimensional states (e.g., entire trajectories) was not only shown to significantly improve accuracy, but also allows planning with information-theoretic objectives, as required for the tasks of active SLAM and information gathering. Nonetheless, planning under this "smoothing" paradigm holds a high computational complexity, which makes it challenging for online solution. Thus, we suggest the following idea: before planning, perform a standalone state variable reordering procedure on the initial belief, and "push forwards" all the predicted loop closing variables. Since the initial variable order determines which subset of them would be affected by incoming updates, such reordering allows us to minimize…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMachine Learning and Algorithms · Bayesian Modeling and Causal Inference · Explainable Artificial Intelligence (XAI)
