D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints
This work addresses the critical problem of efficient and reliable robot navigation in perceptually ambiguous environments, representing an incremental improvement over existing belief space planning methods.
The paper tackles the intractability of belief space planning under ambiguous data association by proposing a method that uses a distilled subset of hypotheses, achieving significant reduction in computation time without compromising solution quality in highly aliased environments.
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.