| Over the past few years, the mobile mapping system (MMS) has become one of the most dominant tools for performing urban road survey. Its captured point cloud has to meet certain accuracies for geomatics applications such as national mapping and infrastructure deformation monitoring and the accuracy strictly relies on a rigorous system calibration scheme. The boresight estimation plays an important role in the overall point cloud quality. Especially when objects are scanned with multiple drive lines and scanners, poor boresight estimation will seriously degrade the accuracy of overlapping of point clouds for the same object . Therefore rigorous, but also cost-effective, boresight calibration methodologies are always desired.;In this thesis, a rigorous boresight self-calibration approach using planar and catenary features that naturally appear in road survey scenes is proposed for boresight calibration of MMSs. Building facades and hanging power cables are two typical examples of the planar and catenary features, respectively, and they can be found in most the modern city roads. The planar and catenary features are both used separately and simultaneously for estimation of the boresights, therefore, the calibration outcome in thesis are classified into three types of calibration: plane-based calibration, catenary-based calibration and mixed feature-based calibration.;A detailed analysis of the calibration is given in this thesis. The calibration results are promising even though they are hindered by lack of some feature geometries. The results also show that both the plane-based and catenary-based calibration have their own shortcomings, but they can be improved with the mixed featured-based calibration. |
