# Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints

arXiv: https://arxiv.org/abs/2207.08096 · HTML: https://mlsanity.com/paper/2207.08096/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 17 Jul 2022.

## Builds on (most cited first, 7 of 7)

- [DESPOT: Online POMDP Planning with Regularization](https://mlsanity.com/paper/1609.03250/) (2016)
- [Modeling Perceptual Aliasing in SLAM via Discrete-Continuous Graphical Models](https://mlsanity.com/paper/1810.11692/) (2018)
- [Simplified decision making in the belief space using belief sparsification](https://mlsanity.com/paper/1909.00885/) (2019)
- [Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency](https://mlsanity.com/paper/2105.14239/) (2021)
- [D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints](https://mlsanity.com/paper/2202.04954/) (2022)
- [Online POMDP Planning via Simplification](https://mlsanity.com/paper/2105.05296/) (2021)
- [iMHS: An Incremental Multi-Hypothesis Smoother](https://mlsanity.com/paper/2103.13178/) (2021)

## Built on by (most cited first, 5 of 5)

- [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)
- [Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees](https://mlsanity.com/paper/2303.02139/) (2023)
- [No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification](https://mlsanity.com/paper/2310.10274/) (2023)
- [Towards Optimal Performance and Action Consistency Guarantees in Dec-POMDPs with Inconsistent Beliefs and Limited Communication](https://mlsanity.com/paper/2512.20778/) (2025)

## Abstract (arXiv)

Autonomous agents operating in perceptually aliased environments should ideally be able to solve the data association problem. Yet, planning for future actions while considering this problem is not trivial. State of the art approaches therefore use multi-modal hypotheses to represent the states of the agent and of the environment. However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the planning horizon. As such, the corresponding Belief Space Planning problem quickly becomes unsolvable. Moreover, under hard computational budget constraints, some non-negligible hypotheses must eventually be pruned in both planning and inference. Nevertheless, the two processes are generally treated separately and the effect of budget constraints in one process over the other was barely studied. We present a computationally efficient method to solve the nonmyopic Belief Space Planning problem while reasoning about data association. Moreover, we rigorously analyze the effects of budget constraints in both inference and planning.