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31 January 2020 A calibration method for automotive augmented reality head-up displays using a chessboard and warping maps
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Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 114332W (2020) https://doi.org/10.1117/12.2557039
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands
Abstract
We propose a calibration method for automotive augmented reality head-up displays (AR-HUD) using a chessboard pattern and warping maps. The HUD is modeled as a pinhole camera whose intrinsic parameters are determined by employing a stereo method. We select several viewpoints within the driver’s eye box and place a smartphone at each of them in sequence, whose position is sensed by a head tracker. By automatically shifting 2D points on the HUD virtual image to 3D chessboard corners within the view of the smartphone camera, we obtain a group of 2D–3D correspondences and then compute view-dependent extrinsic parameters. Using these parameters, we reproject the chessboard corners back to the virtual image. Comparing the results with measured virtual points, we acquire 2D distributions of biases, from which we reconstruct a series of warping maps as a tool for compensating optical distortions. For any other uninvolved viewpoint in the eye box, we obtain its corresponding extrinsic parameters and warping maps through interpolation. Our method outperforms the existing ones in terms of modeling complexity as well as experimental workload. The reprojection errors at 7.5 m distance fall within a few millimeters, which indicates a high augmentation accuracy. Besides, we calibrate the head tracker by utilizing the acquired extrinsic parameters and viewpoint tracking results.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiang Gao, Janis Werner, Marc Necker, and Wilhelm Stork "A calibration method for automotive augmented reality head-up displays using a chessboard and warping maps", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 114332W (31 January 2020); https://doi.org/10.1117/12.2557039
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