D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints

TL;DR

This paper introduces a computationally efficient method for Belief Space Planning that manages data association ambiguities using a distilled subset of hypotheses, providing performance guarantees under budget constraints.

Contribution

It presents a novel approach that reduces hypothesis complexity in BSP with data association, including error bounds for performance guarantees.

Findings

Significantly reduces computation time in highly aliased environments.

Maintains solution quality despite hypothesis pruning.

Provides theoretical error bounds for the approach.

Abstract

Unresolved data association in ambiguous and perceptually aliased environments leads to multi-modal hypotheses on both the robot's and the environment state. To avoid catastrophic results, when operating in such ambiguous environments, it is crucial to reason about data association within Belief Space Planning (BSP). However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the planning horizon and determining the optimal action sequence quickly becomes intractable. Moreover, with hard budget constraints where some non-negligible hypotheses must be pruned, achieving performance guarantees is crucial. In this work we present a computationally efficient novel approach that utilizes only a distilled subset of hypotheses to solve BSP problems while reasoning about data association. Furthermore, to provide performance guarantees, we derive error bounds with respect to the optimal solution. We then demonstrate our approach in an extremely aliased environment, where we manage to significantly reduce computation time without compromising on the quality of the solution.