Matching Feature Points in 3D World
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
This thesis work deals with the most actual topic in Computer Vision field which is scene understanding and this using matching of 3D feature point images. The objective is to make use of Saab’s latest breakthrough in extraction of 3D feature points, to identify the best alignment of at least two 3D feature point images.
The thesis gives a theoretical overview of the latest algorithms used for feature detection, description and matching. The work continues with a brief description of the simultaneous localization and mapping (SLAM) technique, ending with a case study on evaluation of the newly developed software solution for SLAM, called slam6d.
Slam6d is a tool that registers point clouds into a common coordinate system. It does an automatic high-accurate registration of the laser scans. In the case study the use of slam6d is extended in registering 3D feature point images extracted from a stereo camera and the results of registration are analyzed.
In the case study we start with registration of one single 3D feature point image captured from stationary image sensor continuing with registration of multiple images following a trail.
Finally the conclusion from the case study results is that slam6d can register non-laser scan extracted feature point images with high-accuracy in case of single image but it introduces some overlapping results in the case of multiple images following a trail.
Place, publisher, year, edition, pages
2012. , 45 p.
Computer Vision, Edges, Corners, 3D Feature Points, Point Clouds, Simultaneous Localization and Mapping (SLAM), 3D Scene, Iterative Closest Points Algorithm (ICP), Global Matching.
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:hj:diva-23049OAI: oai:DiVA.org:hj-23049DiVA: diva2:686457
Saab Training Systems
Subject / course
JTH, Computer and Electrical Engineering
Johansson, AlfCalås, Göran
Kumar, Shashi, Ph.D.