论文标题
多功能：无目标摄像机校准的多功能边缘对齐
Multi-FEAT: Multi-Feature Edge AlignmenT for Targetless Camera-LiDAR Calibration
论文作者
论文摘要
对汽车和无人机（无人驾驶的Ariel车辆）的准确环境感知依赖于机载传感器的精度，这需要可靠的现场校准。本文介绍了一种新颖的方法，用于无目标的摄像机外部校准，称为多功能（多功能边缘对齐）。多功能使用圆柱投影模型将2D（相机）-3d（LiDAR）校准问题转换为2D-2D校准问题，并利用各种激光雷达功能信息来补充稀疏的LIDAR点云边界。此外，具有精度因子的功能匹配函数旨在改善解决方案空间的平滑度。使用KITTI数据集评估了所提出的多力算法的性能，与几种现有的无目标校准方法相比，我们的方法显示出更可靠的结果。我们总结了我们的结果，并提出了未来工作的潜在方向。
The accurate environment perception of automobiles and UAVs (Unmanned Ariel Vehicles) relies on the precision of onboard sensors, which require reliable in-field calibration. This paper introduces a novel approach for targetless camera-LiDAR extrinsic calibration called Multi-FEAT (Multi-Feature Edge AlignmenT). Multi-FEAT uses the cylindrical projection model to transform the 2D(Camera)-3D(LiDAR) calibration problem into a 2D-2D calibration problem, and exploits various LiDAR feature information to supplement the sparse LiDAR point cloud boundaries. In addition, a feature matching function with a precision factor is designed to improve the smoothness of the solution space. The performance of the proposed Multi-FEAT algorithm is evaluated using the KITTI dataset, and our approach shows more reliable results, as compared with several existing targetless calibration methods. We summarize our results and present potential directions for future work.
