CREPES-X: 階層型方位・距離・慣性による直接協調相対姿勢推定システム
CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System
小型ハードウェアと二段階の階層推定器により、方位外れ値に頑健で高精度なマルチロボット間の相対姿勢推定を実現した。
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著者: Zhehan Li, Zheng Wang, Jiadong Lu, Qi Liu, Zhiren Xun, Yue Wang, Fei Gao, Chao Xu, Yanjun Cao
分類: cs.RO
原文アブストラクト
Relative localization is critical for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental features or inertial assumptions or suffer from non-line-of-sight degradation and outliers in complex environments. Robust and efficient fusion of inter-robot measurements such as bearings, distances, and inertials for tens of robots remains challenging. We present CREPES-X (Cooperative RElative Pose Estimation System with multiple eXtended features), a hierarchical relative localization framework that enhances speed, accuracy, and robustness under challenging conditions, without requiring any global information. CREPES-X starts with a compact hardware design: InfraRed (IR) LEDs, an IR camera, an ultra-wideband module, and an IMU housed in a cube no larger than 6cm on each side. Then CREPES-X implements a two-stage hierarchical estimator to meet different requirements, considering speed, accuracy, and robustness. First, we propose a single-frame relative estimator that provides instant relative poses for multi-robot setups through a closed-form solution and robust bearing outlier rejection. Then a multi-frame relative estimator is designed to offer accurate and robust relative states by exploring IMU pre-integration via robocentric relative kinematics with loosely- and tightly-coupled optimization. Extensive simulations and real-world experiments validate the effectiveness of CREPES-X, showing robustness to up to 90% bearing outliers, proving resilience in challenging conditions, and achieving RMSE of 0.073m and 1.817{\deg} in real-world datasets.
