# Transmitter Discovery through Radio-Visual Probabilistic Active Sensing

arXiv: https://arxiv.org/abs/2103.14965 · HTML: https://mlsanity.com/paper/2103.14965/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 27 Mar 2021.

## Builds on (most cited first, 7 of 7)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search](https://mlsanity.com/paper/1902.10182/) (2019)
- [Collaborative Multi-Robot Systems for Search and Rescue: Coordination and Perception](https://mlsanity.com/paper/2008.12610/) (2020)
- [Probabilistic Radio-Visual Active Sensing for Search and Tracking](https://mlsanity.com/paper/2011.10474/) (2020)
- [Probabilistic RF-Assisted Camera Wake-Up through Self-Supervised Gaussian Process Regression](https://mlsanity.com/paper/2102.03350/) (2021)
- [UAVs using Bayesian Optimization to Locate WiFi Devices](https://mlsanity.com/paper/1510.03592/) (2015)

## Built on by (most cited first, 2 of 2)

- [Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles](https://mlsanity.com/paper/2105.00246/) (2021)
- [Ultra Low-Power, Lightweight, Probabilistic RSS-Based Path Reconstruction: A System for Landscape-Scale Bee Tracking](https://mlsanity.com/paper/2608.27152/) (2026)

## Abstract (arXiv)

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.