Where Am I Now? Dynamically Finding Optimal Sensor States to Minimize Localization Uncertainty for a Perception-Denied Rover

TL;DR

DyFOS is an active perception method that dynamically optimizes sensor states to minimize localization uncertainty for a perception-denied rover, effectively balancing speed and accuracy through a neural network-based prediction pipeline.

Contribution

The paper introduces DyFOS, a novel approach combining neural network predictions and optimization to find optimal sensor states for localization in obstacle-rich environments.

Findings

DyFOS outperforms brute force in speed while maintaining accuracy.

DyFOS achieves lower localization uncertainty than random and heuristic searches.

The method effectively predicts occlusion and collision to improve sensor state selection.

Abstract

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.