iKalibr：面向弹性集成惯性系统的统一无目标时空标定
iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial Systems
AI总结：
针对现有标定方法面向特定系统、忽略时间标定且依赖人工靶标的问题，提出面向弹性集成惯性系统的统一无目标时空标定框架iKalibr，通过动态初始化与连续时间批量优化实现IMU、雷达、LiDAR和相机的准确标定并开源。
摘要：
集成惯性系统通常将IMU与雷达、LiDAR和相机等外部感知传感器集成在一起，已广泛应用于现代机器人应用中的自运动估计、运动控制或自主探索。为了提高系统的准确性、鲁棒性和进一步的可用性，通常会弹性集成多种和多样的传感器，这有利于系统在故障容限、感知能力和环境兼容性方面的性能。对于此类系统，需要准确且一致的时空标定，以维持多传感器融合的统一时空框架。考虑到大多数现有的标定方法（i）通常面向特定的集成惯性系统，（ii）往往只关注空间确定，（iii）通常需要人工标定靶，缺乏便利性和可用性，我们提出了iKalibr：一个面向弹性集成惯性系统的统一无目标时空标定框架，它克服了上述问题，并实现了准确且一致的标定。目前iKalibr共支持四种常用传感器，即IMU、雷达、LiDAR和相机。所提出的方法从严格且高效的动态初始化开始，在此过程中估计器中的所有参数将被准确恢复。随后，进行几次连续时间批量优化，以将初始化的参数优化至更优状态。我们进行了充分的现实世界实验，以验证iKalibr的可行性并评估其标定性能。结果表明，iKalibr能够实现准确的弹性时空标定。我们在 (https://github.com/Unsigned-Long/iKalibr) 开源了我们的实现，以造福研究社区。
英文摘要：
The integrated inertial system, typically integrating an IMU and an exteroceptive sensor such as radar, LiDAR, and camera, has been widely accepted and applied in modern robotic applications for ego-motion estimation, motion control, or autonomous exploration. To improve system accuracy, robustness, and further usability, both multiple and various sensors are generally resiliently integrated, which benefits the system performance regarding failure tolerance, perception capability, and environment compatibility. For such systems, accurate and consistent spatiotemporal calibration is required to maintain a unique spatiotemporal framework for multi-sensor fusion. Considering most existing calibration methods (i) are generally oriented to specific integrated inertial systems, (ii) often only focus on spatial determination, (iii) usually require artificial targets, lacking convenience and usability, we propose iKalibr: a unified targetless spatiotemporal calibration framework for resilient integrated inertial systems, which overcomes the above issues, and enables both accurate and consistent calibration. Altogether four commonly employed sensors are supported in iKalibr currently, namely IMU, radar, LiDAR, and camera. The proposed method starts with a rigorous and efficient dynamic initialization, where all parameters in the estimator would be accurately recovered. Subsequently, several continuous-time batch optimizations are conducted to refine the initialized parameters toward better states. Sufficient real-world experiments were conducted to verify the feasibility and evaluate the calibration performance of iKalibr. The results demonstrate that iKalibr can achieve accurate resilient spatiotemporal calibration. We open-source our implementations at (https://github.com/Unsigned-Long/iKalibr) 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。
