Registration of multiple ToF camera point clouds

Abstract: Buildings, maps and objects et cetera, can be modeled using a computer or reconstructed in 3D by data from different kinds of cameras or laser scanners. This thesis concerns the latter. The recent improvements of Time-of-Flight cameras have brought a number of new interesting research areas to the surface. Registration of several ToF camera point clouds is such an area. A literature study has been made to summarize the research done in the area over the last two decades. The most popular method for registering point clouds, namely the Iterative Closest Point (ICP), has been studied. In addition to this, an error relaxation algorithm was implemented to minimize the accumulated error of the sequential pairwise ICP. A few different real-world test scenarios and one scenario with synthetic data were constructed. These data sets were registered with varying outcome. The obtained camera poses from the sequential ICP were improved by loop closing and error relaxation. The results illustrate the importance of having good initial guesses on the relative transformations to obtain a correct model. Furthermore the strengths and weaknesses of the sequential ICP and the utilized error relaxation method are shown.