arXiv cs.MA· Aditya Iyer, Andy Ye, Akshay Narayan, Nora Ayanian·· 12 小时前AI 评分24
Radio-to-Pose Information：用无线电衰减地图校正多机器人协同定位的规范漂移
Radio-to-Pose Information for Gauge-Drift Correction in Cooperative Localization
AI 导读
研究提出 Radio-to-Pose Information（RPI）接口，将接收功率残差及其位姿导数转换为高斯信息矩阵与向量对，用于校正多机器人协同定位中的规范漂移。在 1000 次模拟高斯试验中，RPI 与联合估计结果一致；在 50×50m 场地内 32 组配对试验中，相比仅用无线电加权相对测量的基线，RPI 将共位 RMSE 从 0.542m 降至 0.140m。
正文 · 原文
Abstract:Cooperative localization uses odometry and inter-robot measurements to estimate the motion of a robot team. These measurements describe only relative geometry, but the team's estimated position and orientation can drift together over time, i.e., gauge drift. In this work, we study correction of this gauge drift using a radio attenuation map registered in the same world coordinate frame used for robot poses. Specifically, we propose Radio-to-Pose Information (RPI), an interface that converts power residuals and their pose derivatives into a Gaussian information matrix and vector pair. RPI builds on the insight that received power is sensitive to shared displacement in the world map. The pose estimator uses the interface together with odometry and inter-robot measurements to update poses and minimize uncertainty. We characterize the shared motions detectable from the map and observed links. For an uncertain shared map, RPI keeps correlations across robots and time and reproduces joint Gaussian pose estimates and covariances. Across $1000$ simulated Gaussian trials, RPI matches the results of joint estimation. Across 32 paired trials with a registered map, RPI reduces common-position RMSE from $0.542$m to $0.140$m in a $50\times50$m arena compared with a baseline that uses radio only to weight relative measurements.
| Subjects: | Robotics (cs.RO); Multiagent Systems (cs.MA) |
| Cite as: | arXiv:2611.08361 [cs.RO] |
| (or arXiv:2611.08361v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08361 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Aditya Iyer [view email]
[v1]
Thu, 5 Nov 2026 17:41:02 UTC (261 KB)
来源：arXiv cs.MA · arxiv.org