  Multi-modal Probabilistic Active Sensing (MMPAS) uses sensor fusion and
probabilistic models to control the perception process of robotic sensing
platforms. MMPAS is successfully employed in environmental exploration,
collaborative mobile robotics, and target tracking, being fostered by the high
performance guarantees on autonomous perception. In this context, we propose a
bi-Radio-Visual PAS scheme to solve the transmitter discovery problem.
Specifically, we firstly exploit the correlation between radio and visual
measurements to learn a target detection model in a self-supervised manner.
Then, the model is combined with antenna radiation anisotropies into a Bayesian
Optimization framework that controls the platform. We show that the proposed
algorithm attains an accuracy of 92%, overcoming two other probabilistic active
sensing baselines.
