State-of-the-art LiDAR calibration frameworks mainly use non-probabilistic registration methods such as Iterative Closest Point (ICP) and its variants. These methods suffer from biased results due to their pair-wise registration procedure as well as their sensitivity to initialization and parameterization. This often leads to misalignments in the calibration process. Probabilistic registration methods compensate for these drawbacks by specifically modeling the probabilistic nature of the observations. This paper presents GMMCalib, an automatic target-based extrinsic calibration approach for multi-LiDAR systems. Using an implementation of a Gaussian Mixture Model (GMM)-based registration method that allows joint registration of multiple point clouds, this data-driven approach is compared to ICP algorithms. We perform simulation experiments using the digital twin of the EDGAR research vehicle and validate the results in a real-world environment. We also address the local minima problem of local registration methods for extrinsic sensor calibration and use a distance-based metric to evaluate the calibration results. Our results show that an increase in robustness against sensor miscalibrations can be achieved by using GMM-based registration algorithms. The code is open source and available on GitHub.

翻译：当前最先进的LiDAR标定框架主要采用非概率配准方法，如迭代最近点算法（ICP）及其变体。这些方法由于采用成对配准流程且对初始化和参数化敏感，往往产生有偏结果，常导致标定过程中的失准问题。概率配准方法通过显式建模观测数据的概率特性弥补了上述缺陷。本文提出GMMCalib——一种面向多LiDAR系统的自动目标式外参标定方法。基于高斯混合模型（GMM）配准方法的实现（支持多帧点云的联合配准），这种数据驱动方法与ICP算法进行了对比。我们利用EDGAR研究车辆的数字孪生体开展仿真实验，并在真实环境中验证了结果。同时，针对局部配准方法在外参标定中的局部极小值问题，本文采用基于距离的度量评估标定结果。实验表明，使用GMM配准算法可增强对传感器失准的鲁棒性。相关代码已在GitHub上开源。