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We develop a robust framework for the registration of light detection and ranging (LiDAR) images with 2-D visual images using a method based on intensity gradients. Our proposed algorithm consists of two steps. In the first step, we extract lines from the digital surface model (DSM) given by the LiDAR image, then we use intensity gradients to register the extracted lines from the LiDAR image onto the visual image to roughly estimate the extrinsic parameters of the calibrated camera. In our approach, we overcome some of the limitations of 3-D reconstruction methods based on the matching of features between the two images. Our algorithm achieves an accuracy for the camera pose recovery of about 98% for the synthetic images tested, and an accuracy of about 95% for the real-world images we tested, which were from the downtown New Orleans area.

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This papers explores the use of an error metric based on intensity gradients in an automatic camera pose recovery method for 2D-3D image registration. The method involves extraction of lines from the 3D image and then uses intensity gradients to register these onto the 2D image.This approach have overcome the limitations of matching the features to register the 2D-3D images. The goal of our algorithm is to estimate pose parameters without any apriori knowledge (GPS) and in less processing time. We demonstrated the validity of our approach by experimenting on perspective view using lines as feature.