一键校准激光雷达与磁传感器，提升复杂环境感知精度。
L2M-Calib: One-key Calibration Method for LiDAR and Multiple Magnetic Sensors
- 联合优化激光雷达与磁传感器的外参及磁传感器内参。
- 在仿真和真实场景中均实现高精度与强鲁棒性校准。
- 适合无人车、AGV等需多模态融合的移动机器人应用。
多模态传感器融合通过利用异构传感模态的互补信息，实现鲁棒的环境感知。然而，精确校准是有效融合的关键前提。本文提出一种名为L2M-Calib的一键校准框架，用于融合磁传感器与激光雷达的系统，联合估计两类传感器间的外参变换及磁传感器的内参畸变参数。磁传感器捕捉环境磁场（AMF）模式，其特性不受几何、纹理、光照和天气影响，适用于复杂环境。然而，由于缺乏有效的校准技术，磁传感在多模态系统中的集成仍不充分。为此，我们采用迭代高斯-牛顿法优化外参，并将内参校准建模为加权岭正则化总体最小二乘（w-RRTLS）问题，增强对测量噪声和病态数据的鲁棒性。在模拟数据集和真实世界实验（包括安装于AGV上的传感器配置）中进行了广泛评估，结果表明该方法在多种环境与工况下均具备高精度与强鲁棒性。
原文摘要 · Abstract (English)
Multimodal sensor fusion enables robust environmental perception by leveraging complementary information from heterogeneous sensing modalities. However, accurate calibration is a critical prerequisite for effective fusion. This paper proposes a novel one-key calibration framework named L2M-Calib for a fused magnetic-LiDAR system, jointly estimating the extrinsic transformation between the two kinds of sensors and the intrinsic distortion parameters of the magnetic sensors. Magnetic sensors capture ambient magnetic field (AMF) patterns, which are invariant to geometry, texture, illumination, and weather, making them suitable for challenging environments. Nonetheless, the integration of magnetic sensing into multimodal systems remains underexplored due to the absence of effective calibration techniques. To address this, we optimize extrinsic parameters using an iterative Gauss-Newton scheme, coupled with the intrinsic calibration as a weighted ridge-regularized total least squares (w-RRTLS) problem, ensuring robustness against measurement noise and ill-conditioned data. Extensive evaluations on both simulated datasets and real-world experiments, including AGV-mounted sensor configurations, demonstrate that our method achieves high calibration accuracy and robustness under various environmental and operational conditions.
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