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本文提出了一种用于自动驾驶的 3D-LiDAR 和姿态传感器的新型三阶段外部校准方法。第一阶段可以通过点云表面特征快速标定传感器之间的外部参数,从而在很短的时间内将外部参数从较大的初始误差缩小到较小的误差范围。第二阶段可以进一步校准基于 LiDAR 建图空间占用的外部参数,同时消除运动失真。最后阶段对自主车辆平面运动引起的z轴误差进行修正,最终得到准确的外参数。具体来说,该方法利用了道路场景的自然特性,使其独立且易于在大规模条件下应用。真实世界数据集的实验结果证明了我们方法的可靠性和准确性。

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SensorsCalibration toolbox v0.1

SensorsCalibration is a simple calibration toolbox and open source project, mainly used for sensor calibration in autonomous driving.

Introduction

Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion may be implemented. Precise calibrations are vital for further processing steps, such as sensor fusion and implementation of algorithms for obstacle detection, localization and mapping, and control. Further, sensor fusion is one of the essential tasks in autonomous driving applications that fuses information obtained from multiple sensors to reduce the uncertainties compared to when sensors are used individually. To solve the problem of sensor calibration for autonomous vehicles, we provide a sensors calibration toolbox. The calibration toolbox can be used to calibrate sensors such as IMU, LiDAR, Camera, and Radar.

Environment(Quick Start)

# pull docker image
sudo docker pull scllovewkf/opencalib:v1
# After the image is pulled down, start the docker image.  /home/sz3/ailab/ =  code root path on your host
docker run -it -v /home/sz3/ailab/:/share scllovewkf/opencalib:v1 /bin/bash

Sensors calibration

This calibration toolbox provides some calibration tools based on road scenes. The specific contents are as follows. If you want to use one of the calibration tools in the list below, you can click the use link to enter the instruction page.

calibration param calibration type calibration method mannual calibration auto calibration usage documentation
camera intrinsice intrinsic target-based camera intrinsic
imu heading extrinsic target-less imu heaidng
lidar2imu extrinsic target-less lidar2imu
lidar2camera extrinsic target-less lidar2camera
lidar2lidar extrinsic target-less lidar2lidar
radar2camera extrinsic target-less radar2camera
radar2lidar extrinsic target-less radar2lidar

Factory calibration

At the same time, the calibration toolbox also provides some factory calibration tools.

calibration board type calibration sensor calibration board pattern remove opencv auto calibration usage documentation
chessboard Camera chessboard factory calib
circle board Camera circle_board factory calib
vertical board Camera vertical board factory calib
apriltag board Camera apriltag board factory calib
aruco marker board Camera aruco marker board factory calib
round hole board Camera and LiDAR round hole board factory calib

Related paper

Related paper available on arxiv:
OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

Citation

If you find this project useful in your research, please consider cite:

@article{opencalib,
    title={OpenCalib: A Multi-sensor Calibration Toolbox for Autonomous Driving},
    author={Yan, Guohang and Liu, Zhuochun and Wang, Chengjie and Shi, Chunlei and Wei, Pengjin and Cai, Xinyu and Ma, Tao and Liu, Zhizheng and Zhong, Zebin and Liu, Yuqian and Zhao, Ming and Ma, Zheng and Li, Yikang},
    journal={arXiv preprint arXiv:2205.14087},
    year={2022},
}

License

SensorsCalibration is released under the Apache 2.0 license.

Contact

If you have questions about this repo, please contact Yan Guohang (yanguohang@pjlab.org.cn). If you need business cooperation, please call 19821266250 (same number on WeChat).

About

本文提出了一种用于自动驾驶的 3D-LiDAR 和姿态传感器的新型三阶段外部校准方法。第一阶段可以通过点云表面特征快速标定传感器之间的外部参数,从而在很短的时间内将外部参数从较大的初始误差缩小到较小的误差范围。第二阶段可以进一步校准基于 LiDAR 建图空间占用的外部参数,同时消除运动失真。最后阶段对自主车辆平面运动引起的z轴误差进行修正,最终得到准确的外参数。具体来说,该方法利用了道路场景的自然特性,使其独立且易于在大规模条件下应用。真实世界数据集的实验结果证明了我们方法的可靠性和准确性。

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