A probabilistic analysis of results of co-registration of aerial and mobile laser-scanned point clouds

A probabilistic analysis of results of co-registration of aerial and mobile laser-scanned point clouds

Anastasiadou, Anastasia (TU Delft Architecture and the Built Environment)

van Oosterom, P.J.M. (mentor)
Verbree, Edward (graduation committee)
Bouma, Teake (graduation committee)
Joosten, Peter (mentor)

Delft University of Technology

Geomatics

2019-07-10

Over the last decades, laser scanners are becoming more and more established for the acquisition of geo-information. Depending on the sensor platform where the laser scanners are mounted, there are MLS, ALS and TLS techniques for both indoor and outdoor environments. The high-quality 3D point clouds produced from laser scanners is an important source of 3D spatial information and it is increasingly used in a wide field of applications from engineering to medical modeling, gaming and agriculture. Although laser scanners provide dense and accurate point clouds, a scene coverage and the creation of a complete 3D representation requires multiple scans of the same area. The procedure integrating multiple scans does not always result to a perfect match. Errors that exist in the datasets or errors in the transformation of datasets create difficulties during the matching procedure. Matching data-sets with the best alignment is a topic that has been researched in many fields and a variety of methods have been analyzed for registering point clouds. A co-registration can be compared to a mathematical model and hence it is important to present not only the functional model and the set of functional relationships between the variables but also the stochastic model that describes the variability among the values and gives insights about the level of satisfying predefined demands. As a result, it becomes necessary to establish an evaluation of the output of a procedure and each result to be accompanied by its quality description. The aim of this graduation thesis is about presenting the stochastic model of a co-registration with a quality description of the result of co-registration between point clouds. The approach will be applicable to results acquired from an image-based registration and the goal is to implement a method in order to quantify the quality of the co-registered result of two different point clouds. A probabilistic analysis of the results of an image-based method results to a quantification of the quality of the output. Response images acquired from an image-based co-registration are tested and the quality indicators of precision and reliability are determined. Moreover, the shift parameter is also defined, fact that enhances the applicability of this research to co-registrations referring to big areas where multiple local registrations have to be combined in a globally consistent manner and each local result contributes to the global registration according to its quality indicators. Particularly, response images that have clearly defined peak areas and pass the tested criteria, are considered more reliable and contribute to the global registration with a greater weight. On the contrary, blurry response images, fail to satisfy the tests, are considered less reliable results and contribute with a smaller weight. The analysis of the images performed by testing the distribution of pixel values in both directions of the images. The implemented approach provides information about the precision and reliability of the images that have normally distributed values. Furthermore, by fitting a Gaussian line to the discrete pixel values, a sub-pixel accuracy of the result is achieved as values are generated in between the discrete pixels. The developed method quantifies the quality of an image-based registration but further improvements and investigation of the recommendations can also attach additional value.

Co-registration
Probabilistic analysis
Quality
Point clouds
Stochastic Analysis

http://resolver.tudelft.nl/uuid:f5429395-475a-4c94-a049-f6038f476805

Student theses

master thesis

© 2019 Anastasia Anastasiadou