外观
论文arXiv 2609.217071 个月前
NeuRIO：用于零样本 Sim-to-Real 多机器人相对惯性里程计的流式神经估计器
原文链接：NeuRIO: A Streaming Neural Estimator for Zero-Shot Sim-to-Real Multi-Robot Relative Inertial Odometry（arXiv 2609.21707 · 发布日期：2026-09-18）
NeuRIO: A Streaming Neural Estimator for Zero-Shot Sim-to-Real Multi-Robot Relative Inertial Odometry
NeuRIO用图网络融合机器人间方位、距离和IMU，实现无锚6-DoF相对惯性里程计，并在真实序列和仿真规模测试中验证。
核心要点
- 多机器人相对惯性里程计需在无锚条件下处理变规模团队和时变观测图。
- 方法将测量规范到重力对齐坐标，以机器人为节点、互观测为因子，用注意力和GRU建模。
- 在24段真实序列上达14.1 cm位置RMSE和3.9°旋转RMSE。
- 仿真中最多400机器人更新低于20 ms，且可直接迁移到未见更大团队。
验证范围与局限：验证范围为24段真实世界序列；最多400机器人的扩展性在仿真中测试。
英文摘要 (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 $14.1,\mathrm{cm}$ position RMSE and $3.9^\circ$ rotation RMSE. More importantly, NeuRIO demonstrates strong computational scalability, maintaining an update cost below $20,\mathrm{ms}$ with up to $400$ robots in simulation, while optimization-based methods exceed $20,\mathrm{ms}$ at only $24$ robots. Moreover, even trained on limited team sizes, NeuRIO transfers directly to unseen larger teams without architectural or parameter changes.
作者：Zhehan Li, Jiadong Lu, Shengwei Ren, Chao Xu, Yanjun Cao
来源
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