  Collaborative multi-robot perception provides multiple views of an
environment, offering varying perspectives to collaboratively understand the
environment even when individual robots have poor points of view or when
occlusions are caused by obstacles. These multiple observations must be
intelligently fused for accurate recognition, and relevant observations need to
be selected in order to allow unnecessary robots to continue on to observe
other targets. This research problem has not been well studied in the
literature yet. In this paper, we propose a novel approach to collaborative
multi-robot perception that simultaneously integrates view selection, feature
selection, and object recognition into a unified regularized optimization
formulation, which uses sparsity-inducing norms to identify the robots with the
most representative views and the modalities with the most discriminative
features. As our optimization formulation is hard to solve due to the
introduced non-smooth norms, we implement a new iterative optimization
algorithm, which is guaranteed to converge to the optimal solution. We evaluate
our approach through a case-study in simulation and on a physical multi-robot
system. Experimental results demonstrate that our approach enables effective
collaborative perception through accurate object recognition and effective view
and feature selection.
