  We present DyFOS, an active perception method that dynamically finds optimal
states to minimize localization uncertainty while avoiding obstacles and
occlusions. We consider the scenario where a perception-denied rover relies on
position and uncertainty measurements from a viewer robot to localize itself
along an obstacle-filled path. The position uncertainty from the viewer's
sensor is a function of the states of the sensor itself, the rover, and the
surrounding environment. To find an optimal sensor state that minimizes the
rover's localization uncertainty, DyFOS uses a localization uncertainty
prediction pipeline in an optimization search. Given numerous samples of the
states mentioned above, the pipeline predicts the rover's localization
uncertainty with the help of a trained, complex state-dependent sensor
measurement model (a probabilistic neural network). Our pipeline also predicts
occlusion and obstacle collision to remove undesirable viewer states and reduce
unnecessary computations. We evaluate the proposed method numerically and in
simulation. Our results show that DyFOS is faster than brute force yet performs
on par. DyFOS also yielded lower localization uncertainties than faster random
and heuristic-based searches.
