Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints

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 art approaches therefore use multi-modal hypotheses represent states agent and environment. However, explicitly all possible associations, number grows exponentially with horizon. As such, corresponding Belief Space Planning quickly becomes unsolvable. Moreover, under hard computational budget constraints, some non-negligible must eventually pruned both inference. Nevertheless, two processes are generally treated separately effect constraints one process over other was barely studied. We present a computationally efficient method nonmyopic reasoning about association. we rigorously analyze effects inference planning.