Abstract

A spherical camera can observe the environment for almost 720 degrees’ field of view in one shoot, which is useful for augmented reality, environment documentation, or mobile mapping applications. This paper aims to develop a spherical photogrammetry imaging system for the purpose of 3D measurement through a backpacked mobile mapping system (MMS). The used equipment contains a Ladybug-5 spherical camera, a tactical grade positioning and orientation system (POS), i.e. SPAN-CPT, and an odometer, etc. This research aims to directly apply photogrammetric space intersection technique for 3D mapping from a spherical image stereo-pair. For this purpose, several systematic calibration procedures are required, including lens distortion calibration, relative orientation calibration, boresight calibration for direct georeferencing, and spherical image calibration. The lens distortion is serious on the ladybug-5 camera’s original 6 images. Meanwhile, for spherical image mosaicking from these original 6 images, we propose the use of their relative orientation and correct their lens distortion at the same time. However, the constructed spherical image still contains systematic error, which will reduce the 3D measurement accuracy. Later for direct georeferencing purpose, we need to establish a ground control field for boresight/lever-arm calibration. Then, we can apply the calibrated parameters to obtain the exterior orientation parameters (EOPs) of all spherical images. In the end, the 3D positioning accuracy after space intersection will be evaluated, including EOPs obtained by structure from motion method.

Highlights

  • In contrast to the traditional stereo pairs of images, the spherical images can provide more comprehensive geospatial information

  • For the purpose of photogrammetric measurement, this paper focus on the issues of spherical image registration method to solve the problems mentioned above

  • This paper suggests a spherical model, a stitching algorithm based on relative orientation of all cameras

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Summary

Introduction

In contrast to the traditional stereo pairs of images, the spherical images can provide more comprehensive geospatial information. The spherical image registration algorithm is the most important part since it will influence the quality of registration and the systematic error. Traditional spherical image registrations are mainly divided into two categories. The first one utilize image matching in the overlapped area to estimate image transformation coefficients. Many algorithms on feature point matching are investigated. Wang et al (2013) modified the SIFT (Lowe, 2004) algorithm to extract conjugate feature points for panoramic image stitching. If the images contain homogeneous area such as water or sky, this kind of method might not work well due to too few or not well distributed feature points

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