RIs-Calib：一种基于连续时间估计的多3D雷达与IMU开源时空校准器
RIs-Calib: An Open-Source Spatiotemporal Calibrator for Multiple 3D Radars and IMUs Based on Continuous-Time Estimation
AI总结：
针对雷达辅助惯性导航系统的时空校准需求，提出基于连续时间估计的多3D雷达与IMU开源校准器RIs-Calib，经仿真与实测试验验证其校准精确一致，无需额外设施或先验知识。
摘要：
辅助惯性导航系统（INS）通常由惯性测量单元（IMU）和外部感知传感器组成，已被公认为一种可行的导航解决方案。与视觉辅助和激光雷达辅助的INS相比，雷达辅助的INS在恶劣天气条件下能实现更优性能，因为雷达采用的低频测量信号在大气气体和降雨中的衰减效应更小。对于这类雷达辅助INS而言，精确的时空变换是实现最优信息融合的基本前提。本研究提出RIs-Calib：一种基于连续时间估计的多3D雷达与IMU时空校准器，可实现精确的时空校准，且无需任何额外人工基础设施或先验知识。该方法首先执行严格且鲁棒的状态初始化流程，随后进行批量优化，所有参数均可稳定地优化至全局最优状态。我们在仿真和真实世界实验中对RIs-Calib进行了验证与评估，结果表明RIs-Calib能够实现精确且一致的校准。我们已在（https://github.com/Unsigned-Long/RIs-Calib）开源实现代码，以惠及研究社区。
英文摘要：
Aided inertial navigation system (INS), typically consisting of an inertial measurement unit (IMU) and an exteroceptive sensor, has been widely accepted as a feasible solution for navigation. Compared with vision-aided and LiDAR-aided INS, radar-aided INS could achieve better performance in adverse weather conditions since the radar utilizes low-frequency measuring signals with less attenuation effect in atmospheric gases and rain. For such a radar-aided INS, accurate spatiotemporal transformation is a fundamental prerequisite to achieving optimal information fusion. In this work, we present RIs-Calib: a spatiotemporal calibrator for multiple 3D radars and IMUs based on continuous-time estimation, which enables accurate spatiotemporal calibration and does not require any additional artificial infrastructure or prior knowledge. Our approach starts with a rigorous and robust procedure for state initialization, followed by batch optimizations, where all parameters can be refined to global optimal states steadily. We validate and evaluate RIs-Calib on both simulated and real-world experiments, and the results demonstrate that RIs-Calib is capable of accurate and consistent calibration. We open-source our implementations at (https://github.com/Unsigned-Long/RIs-Calib) to benefit the research community.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
