Accurate sensor calibration is a prerequisite for multi-sensor perception and localization systems for autonomous vehicles. The intrinsic parameter calibration of the sensor is to obtain the mapping relationship inside the sensor, and the extrinsic parameter calibration is to transform two or more sensors into a unified spatial coordinate system. Most sensors need to be calibrated after installation to ensure the accuracy of sensor measurements. To this end, we present OpenCalib, a calibration toolbox that contains a rich set of various sensor calibration methods. OpenCalib covers manual calibration tools, automatic calibration tools, factory calibration tools, and online calibration tools for different application scenarios. At the same time, to evaluate the calibration accuracy and subsequently improve the accuracy of the calibration algorithm, we released a corresponding benchmark dataset. This paper introduces various features and calibration methods of this toolbox. To our knowledge, this is the first open-sourced calibration codebase containing the full set of autonomous-driving-related calibration approaches in this area. We wish that the toolbox could be helpful to autonomous driving researchers. We have open-sourced our code on GitHub to benefit the community. Code is available at https://github.com/PJLab-ADG/SensorsCalibration.
翻译:精密传感器校准是多传感器感知和自主车辆定位系统的一个先决条件。传感器的内在参数校准是获得传感器内部的映像关系,而外源参数校准是将两个或两个以上的传感器转换成统一的空间坐标系统。大多数传感器需要在安装后进行校准,以确保传感器测量的准确性。为此,我们介绍OpenCalib,一个校准工具箱,包含丰富的各种传感器校准方法。OpenCalib包括手动校准工具、自动校准工具、工厂校准工具和不同应用情景的在线校准工具。与此同时,为了评估校准准确性,并随后提高校准算法的准确性,我们发布了一个相应的基准数据集。本文介绍了该工具箱的各种特征和校准方法。据我们所知,这是第一个包含全套自动驾驶相关校准方法的开源校准代码库。我们希望该工具箱能够帮助自主驾驶研究人员。我们在GiSA/DGA/Lub上打开了我们的代码源码。