We propose a calibration method for automotive augmented reality head-up displays (AR-HUD) using a chessboardpattern and warping maps. The HUD is modeled as a pinhole camera whose intrinsic parameters are determined byemploying a stereo method. We select several viewpoints within the driver’s eye box and place a smartphone at each ofthem in sequence, whose position is sensed by a head tracker. By automatically shifting 2D points on the HUD virtualimage to 3D chessboard corners within the view of the smartphone camera, we obtain a group of 2D–3D correspondencesand then compute view-dependent extrinsic parameters. Using these parameters, we reproject the chessboard corners backto the virtual image. Comparing the results with measured virtual points, we acquire 2D distributions of biases, from whichwe reconstruct a series of warping maps as a tool for compensating optical distortions. For any other uninvolved viewpointin the eye box, we obtain its corresponding extrinsic parameters and warping maps through interpolation. Our methodoutperforms the existing ones in terms of modeling complexity as well as experimental workload. The reprojection errorsat 7.5m distance fall within a few millimeters, which indicates a high augmentation accuracy. Besides, we calibrate thehead tracker by utilizing the acquired extrinsic parameters and viewpoint tracking results.
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