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

Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach