Techniques for Extracting Contours and Merging Maps

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Dissertation Defense, Nov 26, 2008, 10:00AM – 11:30AM, Wachman 447

Techniques for Extracting Contours and Merging Maps

Nagesh Adluru


Dr. Longin Jan Latecki, Adviser (Computer & Information Sciences)
Dr. Rolf Lakamper (Computer & Information Sciences)
Dr. Slobodan Vucetic (Computer & Information Sciences)
Dr. Marc Sobel (Statistics)

Contour grouping and robot mapping are central components for two important open problems viz. object recognition and autonomous navigation that remain central in robotic, or in other words, computational intelligence. Object boundaries are very useful descriptors for recognizing objects. Extracting boundaries from real images has been a notoriously open problem for several decades in the vision community. We developed novel techniques for extracting object boundaries. The techniques are based on practically successful state-of-the-art Bayesian filtering framework, well-founded geometric properties relating boundaries and skeletons and robust high-level shape analyses.
Acquiring global maps of the environments is crucial for robots to localize and be able to navigate autonomously. Though there has been a lot of progress in achieving autonomous mobility, for e.g. as in DARPA grand-challenges of 2005 and 2007, the mapping problem itself remains to be open which is essential for robust autonomy in hard cases like rescue arenas and collaborative exploration. We developed physics-based energy minimization techniques and also shape based techniques for scalable alignment of maps. Our shape based techniques are a product of combining of high-level vision techniques that exploit similarities among maps and strong statistical methods that can handle uncertainties in Bayesian sense.