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This research introduces SHARE, a novel SLAM framework that optimizes latency in shared human-robot workspaces by prioritizing the stringent timing needs of head-mounted AR users over robotic agents. By leveraging visual feature redundancy to reduce edge processing burdens, the system achieves an average end-to-end latency of 13.22 ms for humans while maintaining high tracking accuracy. This advancement addresses a critical bottleneck in mixed-reality collaboration where uniform resource allocation previously caused unacceptable lag for interactive manipulation tasks.
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SHARE: Towards Head-Mounted AR with User-Centric SLAM in Shared Human-Robot Workspaces

arXiv3 min read

arXiv:2607.23901v1 Announce Type: cross Abstract: Human-Robot Collaboration (HRC) in shared physical spaces using Augmented Reality (AR) interfaces is powered by Simultaneous Localization and Mapping (SLAM). Existing multi-agent SLAM systems rely on an edge server to combine visual findings of multiple resource-constrained agents, perform computation, and schedule updates to their local maps. However, the edge treats all agents uniformly and ignores the fundamentally different latency requirements of heterogeneous HRC agents: robots and head-mounted AR users. This uniform resource allocation often results in high lag for user manipulation, as it does not meet the stringent latency requirements of AR. In this work, we design, implement, and evaluate SHARE, a user-centric SLAM system that strategically prioritizes AR user experience while maintaining accurate tracking performance for robots. SHARE builds a first-of-its-kind experience model for HRC agents and adaptively adjusts transmission priorities to match it. To reduce end-to-end latency, SHARE leverages the redundancy of visual features acquired by agents in shared human-robot workspaces to reduce computation time induced by edge-based processing. Real-world deployment with commercial AR headsets and a ground robot achieves 13.22 ms average latency for AR users (43.3% reduction from baseline) while maintaining sub-2-centimeter tracking accuracy. User studies further reveal statistically significant improvements in user perception.

Abstract:Human-Robot Collaboration (HRC) in shared physical spaces using Augmented Reality (AR) interfaces is powered by Simultaneous Localization and Mapping (SLAM). Existing multi-agent SLAM systems rely on an edge server to combine visual findings of multiple resource-constrained agents, perform computation, and schedule updates to their local maps. However, the edge treats all agents uniformly and ignores the fundamentally different latency requirements of heterogeneous HRC agents: robots and head-mounted AR users. This uniform resource allocation often results in high lag for user manipulation, as it does not meet the stringent latency requirements of AR. In this work, we design, implement, and evaluate SHARE, a user-centric SLAM system that strategically prioritizes AR user experience while maintaining accurate tracking performance for robots. SHARE builds a first-of-its-kind experience model for HRC agents and adaptively adjusts transmission priorities to match it. To reduce end-to-end latency, SHARE leverages the redundancy of visual features acquired by agents in shared human-robot workspaces to reduce computation time induced by edge-based processing. Real-world deployment with commercial AR headsets and a ground robot achieves 13.22 ms average latency for AR users (43.3% reduction from baseline) while maintaining sub-2-centimeter tracking accuracy. User studies further reveal statistically significant improvements in user perception.

Human-Computer Interaction (cs.HC); Robotics (cs.RO)

arXiv:2607.23901 [cs.HC]

(or arXiv:2607.23901v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2607.23901 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1145/3832004 Focus to learn more DOI(s) linking to related resources

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Full text under the publisher's open licence · 304 words

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