Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation

Abstract

We address the problem of sparse selection of visual features for localizing a team of robots navigating in an unknown environment, where robots can exchange relative position measurements with neighbors. We select a set of the most informative features by anticipating their importance in robots localization by simulating trajectories of robots over a prediction horizon. Through theoretical proofs, we establish a crucial connection between graph Laplacian and the importance of features. We leverage a scalable randomized algorithm for sparse sums of positive semidefinite matrices to efficiently select a set of the most informative features.