Transmitter Discovery through Radio-Visual Probabilistic Active Sensing
This work addresses the problem of autonomous transmitter discovery for robotic sensing platforms, representing an incremental improvement in multi-modal active sensing.
The paper tackled the transmitter discovery problem by proposing a bi-Radio-Visual Probabilistic Active Sensing scheme, which achieved 92% accuracy and outperformed two baseline methods.
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.