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Automatic Calibration for Road Side Lidar and Camera Using Planar Target
Technical Paper
2021-01-7023
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
In recent years, vehicle-intelligent road cooperation is gaining an increasing attention from both academia and industry, which require deployment of a large scale of road side sensors such as lidar and camera. For the road side sensors, calibration is indispensable to obtain transformation between sensor coordinate frame and geographic coordinate frame. Currently, manual measuring using RTK and marking correspondent feature points in sensors’ field of view is the commonly used method of calibration, which is far too complicated. To simplify the calibration task and improve efficiency, an automatic calibration method for road side lidar and camera using a planar calibration target is proposed in this paper. The feature of planar target is designed to be easily identified by the sensors, and an Integrated Navigation System (INS), which acquires the geographic coordinate of itself in real time at an accuracy of centimeter level, is fixed on the target. The calibration target as well as the INS is fixed on a movable platform to improve mobility and thus improve the efficiency of data collection. Then automatic feature extraction procedure is conducted to detect feature points from point cloud and image generated from sensors. And coordinate transformation procedure is conducted to calculate the geographic coordinates of feature points on calibration target from coordinates of INS. Thus, the 2D and 3D feature points coordinates of target as well as geographic coordinates are able to be extracted and matched with each other automatically, and finally the calibration procedure is conducted. The experiment results show that the proposed calibration method is able to calibrate road side lidar and camera efficiently and accurately.
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Citation
Wang, Q., Wang, Y., Zhang, Y., and Yin, C., "Automatic Calibration for Road Side Lidar and Camera Using Planar Target," SAE Technical Paper 2021-01-7023, 2021, https://doi.org/10.4271/2021-01-7023.Also In
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