Mutual localization plays a crucial role in multi-robot cooperation. CREPES, a novel system that focuses on six degrees of freedom (DOF) relative pose estimation for multi-robot systems, is proposed in this paper. CREPES has a compact hardware design using active infrared (IR) LEDs, an IR fish-eye camera, an ultra-wideband (UWB) module and an inertial measurement unit (IMU). By leveraging IR light communication, the system solves data association between visual detection and UWB ranging. Ranging measurements from the UWB and directional information from the camera offer relative 3-DOF position estimation. Combining the mutual relative position with neighbors and the gravity constraints provided by IMUs, we can estimate the 6-DOF relative pose from a single frame of sensor measurements. In addition, we design an estimator based on the error-state Kalman filter (ESKF) to enhance system accuracy and robustness. When multiple neighbors are available, a Pose Graph Optimization (PGO) algorithm is applied to further improve system accuracy. We conduct enormous experiments to demonstrate CREPES' accuracy between robot pairs and a team of robots, as well as performance under challenging conditions.

翻译：相互定位在多机器人协作中起着关键作用。本文提出了CREPES，一种专注于多机器人系统六自由度（DOF）相对位姿估计的新型系统。CREPES采用紧凑的硬件设计，集成了主动式红外（IR）LED、红外鱼眼相机、超宽带（UWB）模块和惯性测量单元（IMU）。通过利用红外光通信，该系统解决了视觉检测与UWB测距之间的数据关联问题。来自UWB的测距测量和来自相机的方向信息提供了相对的三自由度位置估计。结合相邻机器人之间的相互相对位置和IMU提供的重力约束，我们可以从单帧传感器测量中估计出六自由度相对位姿。此外，我们设计了一种基于误差状态卡尔曼滤波器（ESKF）的估计器，以增强系统的精度和鲁棒性。当存在多个相邻机器人时，应用位姿图优化（PGO）算法以进一步提高系统精度。我们开展了大量实验，验证了CREPES在机器人对和机器人团队中的精度，以及在挑战性条件下的性能。