Properly-calibrated sensors are the prerequisite for a dependable autonomous driving system. However, most prior methods focus on extrinsic calibration between sensors, and few focus on the misalignment between the sensors and the vehicle coordinate system. Existing targetless approaches rely on specific prior knowledge, such as driving routes and road features, to handle this misalignment. This work removes these limitations and proposes more general calibration methods for four commonly used sensors: Camera, LiDAR, GNSS/INS, and millimeter-wave Radar. By utilizing sensor-specific patterns: image feature, 3D LiDAR points, GNSS/INS solved pose, and radar speed, we design four corresponding methods to mainly calibrate the rotation from sensor to car during normal driving within minutes, composing a toolbox named SensorX2car. Real-world and simulated experiments demonstrate the practicality of our proposed methods. Meanwhile, the related codes have been open-sourced to benefit the community. To the best of our knowledge, SensorX2car is the first open-source sensor-to-car calibration toolbox. The code is available at https://github.com/OpenCalib/SensorX2car.

翻译：精确标定的传感器是可靠自动驾驶系统的前提条件。然而，现有方法大多专注于传感器间的外参标定，鲜有涉及传感器与车辆坐标系之间的失准问题。现有无靶标方法依赖特定先验知识（如行驶路线和道路特征）来处理此类失准。本研究消除了这些局限性，提出了针对四种常用传感器（摄像头、激光雷达、GNSS/INS及毫米波雷达）的更通用标定方法。通过利用传感器特有模式——图像特征、三维激光雷达点云、GNSS/INS解算位姿及雷达速度——我们设计了四种相应方法，可在正常行驶数分钟内完成从传感器到车辆的旋转标定，共同构成名为SensorX2car的工具箱。真实场景与仿真实验验证了所提方法的实用性，相关代码已开源以惠及学界。据我们所知，SensorX2car是首个开源传感器至车辆标定工具箱。代码见https://github.com/OpenCalib/SensorX2car。