4 accepted papers
Safe navigation in uncertain environments requires planning methods that integrate risk aversion with active perception. In this work, we present a unified frame- work that refines a coarse reference path by construct- ing tail-sensitive risk maps from Average Value-at-Risk statistics on an online-u…
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 loc…
We consider the robot localization problem with sparse visual feature selection. The underlying key property is that contributions of trackable features (landmarks) appear linearly in the information matrix of the corresponding estimation problem. We utilize standard models for motion and vision sys
We investigate a classification problem using multiple mobile agents capable of collecting (partial) pose-dependent observations of an unknown environment. The objective is to classify an image over a finite time horizon. We propose a network architecture on how agents should form a local belief, ta…