  Accurate localization in diverse environments is a fundamental challenge in
computer vision and robotics. The task involves determining a sensor's precise
position and orientation, typically a camera, within a given space. Traditional
localization methods often rely on passive sensing, which may struggle in
scenarios with limited features or dynamic environments. In response, this
paper explores the domain of active localization, emphasizing the importance of
viewpoint selection to enhance localization accuracy. Our contributions involve
using a data-driven approach with a simple architecture designed for real-time
operation, a self-supervised data training method, and the capability to
consistently integrate our map into a planning framework tailored for
real-world robotics applications. Our results demonstrate that our method
performs better than the existing one, targeting similar problems and
generalizing on synthetic and real data. We also release an open-source
implementation to benefit the community.
