NeuRIO: A Streaming Neural Estimator for Zero-Shot Sim-to-Real Multi-Robot Relative Inertial Odometry

arXiv:2609.21707v1 Announce Type: new Abstract: We present NeuRIO, a streaming neural estimator for anchor-free 6-DoF relative inertial odometry using only identified inter-robot bearings, ranges, and IMU measurements. NeuRIO canonicalizes measurements into gravity-aligned coordinates, represents robots as nodes and mutual observations as factors, and uses attention for spatial reasoning and GRUs for temporal modeling. As a graph network, NeuRIO applies shared node-wise and factor-wise operators throughout the network, enabling it to handle different team sizes and time-varying observation graphs. NeuRIO is trained on a simulator that couples various motion patterns, device-level sensor characteristics, and diverse, realistic modeled, and temporally persistent sensor corruptions. In this way, NeuRIO achieves zero-shot sim-to-real transfer. Across 24 real-world sequences, NeuRIO achieves 14.1,cm position RMSE and 3.9^irc rotation RMSE. More importantly, NeuRIO demonstrates strong computational scalability, maintaining an update cost below 20,ms with up to 400 robots in simulation, while optimization-based methods exceed 20,ms at only 24 robots. Moreover, even trained on limited team sizes, NeuRIO transfers directly to unseen larger teams without architectural or parameter changes.

Source: arXiv cs.RO | 2026-09-21