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

arXiv: https://arxiv.org/abs/2202.04954 · HTML: https://mlsanity.com/paper/2202.04954/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 27 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 4 of 4)

- [Simplified decision making in the belief space using belief sparsification](https://mlsanity.com/paper/1909.00885/) (2019)
- [Online POMDP Planning via Simplification](https://mlsanity.com/paper/2105.05296/) (2021)
- [Topological Information-Theoretic Belief Space Planning with Optimality Guarantees](https://mlsanity.com/paper/1903.00927/) (2019)
- [Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty](https://mlsanity.com/paper/2105.05789/) (2021)

## Built on by (most cited first, 6 of 6)

- [Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints](https://mlsanity.com/paper/2207.08096/) (2022)
- [Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints](https://mlsanity.com/paper/2302.06697/) (2023)
- [Monte Carlo Planning in Hybrid Belief POMDPs](https://mlsanity.com/paper/2211.07735/) (2022)
- [Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice](https://mlsanity.com/paper/2311.07745/) (2023)
- [Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees](https://mlsanity.com/paper/2303.02139/) (2023)
- [Resolving Multiple-Dynamic Model Uncertainty in Hypothesis-Driven Belief-MDPs](https://mlsanity.com/paper/2411.14404/) (2024)

## Abstract (arXiv)

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.