论文标题
COOON：在路现场的自动多大校准和改进方法
CROON: Automatic Multi-LiDAR Calibration and Refinement Method in Road Scene
论文作者
论文摘要
基于传感器的环境感知是自动驾驶系统的关键部分。为了对周围环境产生极大的看法，智能系统将配置多个激光射击（3D灯检测和范围），以覆盖汽车的遥远和近距离空间。感知的精度取决于传感器校准的质量。这项研究旨在为一般道路现场的多个LiDAR系统制定准确，自动和可靠的校准策略。因此，我们提出了COOON（在道路场景中自动多弹药校准和改进方法），这是一种两阶段的方法，包括粗糙和改进的校准。第一阶段可以从任意初始姿势校准传感器，第二阶段能够精确地迭代地校准传感器。具体而言，Croon利用道路场景的自然特征，使其独立且易于在大规模条件下应用。现实世界和模拟数据集的实验结果证明了我们方法的可靠性和准确性。所有相关的数据集和代码均在GitHub网站https://github.com/opencalib/lidar2lidar上开源。
Sensor-based environmental perception is a crucial part of the autonomous driving system. In order to get an excellent perception of the surrounding environment, an intelligent system would configure multiple LiDARs (3D Light Detection and Ranging) to cover the distant and near space of the car. The precision of perception relies on the quality of sensor calibration. This research aims at developing an accurate, automatic, and robust calibration strategy for multiple LiDAR systems in the general road scene. We thus propose CROON (automatiC multi-LiDAR CalibratiOn and Refinement method in rOad sceNe), a two-stage method including rough and refinement calibration. The first stage can calibrate the sensor from an arbitrary initial pose, and the second stage is able to precisely calibrate the sensor iteratively. Specifically, CROON utilize the nature characteristics of road scene so that it is independent and easy to apply in large-scale conditions. Experimental results on real-world and simulated data sets demonstrate the reliability and accuracy of our method. All the related data sets and codes are open-sourced on the Github website https://github.com/OpenCalib/LiDAR2LiDAR.
