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.

翻译：在多样化环境中实现精确定位是计算机视觉与机器人学领域的核心挑战。该任务涉及确定传感器（通常为相机）在给定空间内的精确位置与朝向。传统定位方法多依赖于被动感知，在特征稀缺或动态环境中常面临困难。为此，本文探索主动定位领域，强调视点选择对提升定位精度的重要性。我们的贡献包括：采用数据驱动方法并设计适用于实时运行的简洁架构，提出自监督数据训练策略，以及实现将地图持续集成至面向真实机器人应用的规划框架的能力。实验结果表明，本方法在针对同类问题的现有方法中表现更优，且在合成数据与真实数据上均展现良好泛化性能。我们同时开源了实现代码以促进领域发展。