Rather than having each newly deployed robot create its own map of its surroundings, the growing availability of SLAM-enabled devices provides the option of simply localizing in a map of another robot or device. In cases such as multi-robot or human-robot collaboration, localizing all agents in the same map is even necessary. However, localizing e.g. a ground robot in the map of a drone or head-mounted MR headset presents unique challenges due to viewpoint changes. This work investigates how active visual localization can be used to overcome such challenges of viewpoint changes. Specifically, we focus on the problem of selecting the optimal viewpoint at a given location. We compare existing approaches in the literature with additional proposed baselines and propose a novel data-driven approach. The result demonstrates the superior performance of the data-driven approach when compared to existing methods, both in controlled simulation experiments and real-world deployment.

翻译：不同于每台新部署的机器人均需自行构建环境地图，随着支持SLAM设备的日益普及，现有技术提供了直接在其他机器人或设备的地图中进行定位的选择。在多机器人或人机协作场景中，将所有智能体定位在同一张地图上甚至成为必要前提。然而，例如将地面机器人定位在无人机或头戴式混合现实头显的地图中时，视角变化会带来独特挑战。本研究探讨如何利用主动视觉定位克服此类视角变化带来的难题，具体聚焦于在给定位置选择最优视点的问题。我们将现有文献中的方法与额外提出的基线进行对比，并提出一种新颖的数据驱动方法。实验结果表明，无论是在受控仿真实验还是实际部署场景中，该方法相比现有方法均展现出更优的性能。