Open AccessA Modified Online Intelligent Method to Calibrate Radar and Camera Sensors for Data Fusing
Author(s) -
Guobin Xu,
Dongdong Li,
Xiangyang Chen,
Qiankun Li,
Jianqin Yin
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2025/1/012007
Subject(s) - calibration , artificial intelligence , computer science , computer vision , radar , camera auto calibration , camera resectioning , sensor fusion , field (mathematics) , remote sensing , artificial neural network , pixel , geography , mathematics , telecommunications , statistics , pure mathematics
This paper introduces a modified online intelligent calibration method to calibrate radar and camera sensors in traffic scenarios. Radar and camera sensor calibration is to construct a one-to-one mapping between the radar coordinate system and the image pixel coordinate system. Based on the currently widely used artificial intelligence method (deep neural networks), camera can accurately recognize the target type. For the research and application of radar sensor and camera sensor fusion, the most important thing is to synchronize the sensor sampling time of the radar and the camera and space calibration. This paper emphasizes on spatial calibration. At present, the common calibration methods are the calibration method based on the internal and external parameters of the camera, the four-point calibration method, and the neural network calibration method. However, these three calibration methods have significant advantages and disadvantages. In the field of transportation, the disadvantages are even more prominent. This paper proposes a modified online intelligent calibration method for the application field of radar and camera sensors in traffic scenarios. We have proved through simulation experiments that this method is less time-consuming and manual operation while ensuring high-precision calibration performance.