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The plethora of sensors in our commodity devices provides a rich substratefor sensor-fused tracking. Yet, today's solutions are unable to deliver robustand high tracking accuracies across multiple agents in practical, everydayenvironments - a feature central to the future of immersive and collaborativeapplications. This can be attributed to the limited scope of diversityleveraged by these fusion solutions, preventing them from catering to themultiple dimensions of accuracy, robustness (diverse environmental conditions)and scalability (multiple agents) simultaneously. In this work, we take animportant step towards this goal by introducing the notion of dual-layerdiversity to the problem of sensor fusion in multi-agent tracking. Wedemonstrate that the fusion of complementary tracking modalities, -passive/relative (e.g., visual odometry) and active/absolute tracking (e.g.,infrastructure-assisted RF localization) offer a key first layer of diversitythat brings scalability while the second layer of diversity lies in themethodology of fusion, where we bring together the complementary strengths ofalgorithmic (for robustness) and data-driven (for accuracy) approaches. RoVaRis an embodiment of such a dual-layer diversity approach that intelligentlyattends to cross-modal information using algorithmic and data-driven techniquesthat jointly share the burden of accurately tracking multiple agents in thewild. Extensive evaluations reveal RoVaR's multi-dimensional benefits in termsof tracking accuracy (median of 15cm), robustness (in unseen environments),light weight (runs in real-time on mobile platforms such as Jetson Nano/TX2),to enable practical multi-agent immersive applications in everydayenvironments.

RoVaR: Robust Multi-agent Tracking through Dual-layer Diversity in Visual and RF Sensor Fusion