Registration of Panoramic/Fish-Eye Image Sequence and LiDAR Points Using Skyline Features

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 5; p. 1651
• Main Authors: Zhu, Ningning, Jia, Yonghong, Ji, Shunping
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21-05-2018; MDPI
• Subjects: Accuracy; Archives & records; Attitudes; Automation; brute force optimization; Cameras; Control methods; Image registration; Methods; MMS LiDAR points; Pattern recognition; Pixels; Registration; Remote sensing; sequence of panorama/fish-eye images; skyline-based registration; Statistical analysis; Vehicles; United States > US; China; MMS LiDAR points; skyline-based registration; sequence of panorama/fish-eye images; brute force optimization
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18051651

We propose utilizing a rigorous registration model and a skyline-based method for automatic registration of LiDAR points and a sequence of panoramic/fish-eye images in a mobile mapping system (MMS). This method can automatically optimize original registration parameters and avoid the use of manual interventions in control point-based registration methods. First, the rigorous registration model between the LiDAR points and the panoramic/fish-eye image was built. Second, skyline pixels from panoramic/fish-eye images and skyline points from the MMS's LiDAR points were extracted, relying on the difference in the pixel values and the registration model, respectively. Third, a brute force optimization method was used to search for optimal matching parameters between skyline pixels and skyline points. In the experiments, the original registration method and the control point registration method were used to compare the accuracy of our method with a sequence of panoramic/fish-eye images. The result showed: (1) the panoramic/fish-eye image registration model is effective and can achieve high-precision registration of the image and the MMS's LiDAR points; (2) the skyline-based registration method can automatically optimize the initial attitude parameters, realizing a high-precision registration of a panoramic/fish-eye image and the MMS's LiDAR points; and (3) the attitude correction values of the sequences of panoramic/fish-eye images are different, and the values must be solved one by one.