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This paper considers the problem of task-dependent (top-down) attentionallocation for vision-based autonomous navigation using known landmarks. Unlikethe existing paradigm in which landmark selection is formulated as acombinatorial optimization problem, we model it as a resource allocationproblem where the decision-maker (DM) is granted extra freedom to control thedegree of attention to each landmark. The total resource available to DM isexpressed in terms of the capacity limit of the in-take information flow, whichis quantified by the directed information from the state of the environment tothe DM's observation. We consider a receding horizon implementation of such acontrolled sensing scheme in the Linear-Quadratic-Gaussian (LQG) regime. Theconvex-concave procedure is applied in each time step, whose time complexity isshown to be linear in the horizon length if the alternating direction method ofmultipliers (ADMM) is used. Numerical studies show that the proposedformulation is sparsity-promoting in the sense that it tends to allocate zerodata rate to uninformative landmarks.

Dynamic Allocation of Visual Attention for Vision-based Autonomous Navigation under Data Rate Constraints