Registration of Terrestrial LiDAR and Panoramic Imagery Using the Spherical Epipolar Line and Spherical Absolute Orientation Model

Registration between terrestrial LiDAR and optical imagery plays a crucial role in information fusion. However, it is difficult to find reliable correlations among the different feature information of optical imagery and LiDAR point clouds. Therefore, in order to achieve high-precision registration of heterogeneous sensors, a method based on spherical epipolar line and spherical absolute orientation is proposed in this paper. The method firstly projects the LiDAR point clouds into spherical images based on the spherical imaging model and derives the spherical epipolar line equation. Then the relative and absolute orientations of the spherical LiDAR images and the optical images are performed based on manually selected control points. Finally, based on Harris corner extraction, combined with the geometric constraints of the spherical epipolar line and absolute orientation, dense matching between optical and LIDAR images are achieved, and all matching points are used as control points for registration to improve the accuracy of manually selected points registration. Multiple sets of test data are acquired outdoors using a FARO Focus S laser scanner, a Z + F IMAGER 5010C laser scanner, and a Ladybug5+ panoramic camera. The experimental results show that the method in this paper is practical and improves the accuracy of manual points selection registration, and the degree of improvement is related to the number of successfully matched corner points.

I. Introduction

Terrestrial LiDAR and optical imagery are two different ways to capture information about objects of interest. The combination of these two information sources is useful for further analysis, investigation, measurement, and reconstruction. For example, the popular applications and research hotspots in recent years: merging images and LiDAR to achieve complete and accurate three-dimensional (3D) scene reconstruction, are inseparable from the registration of heterogeneous sensors, and many scholars have already carried out related research in this area [1]–[3]. However, due to the different information obtained by the optical and LiDAR imaging systems, registration of these heterogeneous sensors is a challenging task. In order to combine these different data sources precisely, it is necessary to provide a sufficient number of evenly distributed corresponding feature points. Most of the current registration methods are mainly concerned with geometric constraint registration or gray-level similarity based registration, and can be divided into point-based registration [4]–[6], structural-based registration [7]–[9], and mutual information (MI)-based registration [10]–[12] methods.