CREPES-X Robocentric Relative State Estimation System

Paper context

Paper title: CREPES-X: Hierarchical Bearing-Distance-Inertial Direct Cooperative Relative Pose Estimation System Abstract: 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, pr Passages referencing this figure: Figure 1: CREPES-X works in a robocentric frame, independent of the environment, and provides accurate and robust relative state estimation in real-time. In this paper, we introduce CREPES-X, a fully improved cooperative relative pose estimation system with a hierarchical structure to produce multi-layered, accurate, robust relative states within inter-robot mutual observations and overcome the ON 3 challenges, shown in Fig. 1 . IV Hardware Design and Implementation Figure 4: Hardware Comparison IV-A Hardware Fig. 1 (a) shows an overview of our hardware design, which integrates four complementary sensing modalities to achieve robust performance.