Robot Vision Applications using the CSEM SwissRanger Camera 
 Abstract  The SwissRanger is new type of depth vision camera using the TimeofFlight (TOF) principle. It acquires in real time both normal intensity images and 3D range images. It is an active range finder with a harmless light source emitting near infrared light at under {1W}. Most other active range finders are laser based and have much higher latency. The SwissRangers usefulness is proved here by solving two diverse robot vision applications: The mobile robot localization problem and the 3D object pose estimation problem.
The robots localization is found by segmenting range images; into planar surfaces. The segmentation is done by calculating the local surface normals at each pixel, grouping the image into regions and robustly fittting to planes using {RANSAC}. From these planes a map of the robots environment is constructed. For a robot to handle an object it has to recognize the objects pose or orientation in space. This is approached by using a dimensionality reduction method called Local Linear Embedding (LLE). A dataset, with range images of an object can be seen as points in a very high dimensional pixel space. It has been shown that for {3D} objects such points lie on nonlinear manifolds in the high dimensional space. The {LLE} technique reduces the dimensionality down to a true dimensionality of the manifold and reveals the separating characteristic in each point namely its pose. The pose of a new objects can then be detected by mapping it to this low dimensional space.  Keywords  mathematical modelling, 3D imaging, timeofflight, range images, robot localization, pose estimation, range image segmentation, nonlinear dimension reduction, Local Linear Embedding  Type  Master's thesis [Academic thesis]  Year  2006 Month August pp. 99 Ed. 1  Publisher  Informatics and Mathematical Modelling, Technical University of Denmark,DTU  ISBN / ISSN  8764300951  Note  Supervised Rasmus Larsen, and cosupervisor Jens Michael Carstensen, {IMM}.  Electronic version(s)  [pdf]  BibTeX data  [bibtex]  IMM Group(s)  Image Analysis & Computer Graphics 
