Active Visual Localization for Multi-Agent Collaboration: A Data-Driven Approach
This work addresses a domain-specific problem for multi-robot or human-robot collaboration, offering an incremental improvement over existing methods.
The paper tackles the challenge of viewpoint changes in multi-agent visual localization by proposing a data-driven approach for selecting optimal viewpoints, demonstrating superior performance in both simulation and real-world deployments.
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.