Metrics and Optimization Techniques for Registration of Color to Laser Range Scans

Authors:
Chad Hantak;Anselmo Lastra
Affiliations:
UNC at Chapel Hill, USA;UNC at Chapel Hill, USA
Venue:
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Year:
2006

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Abstract

We found previous intensity-based techniques for automatically registering color images to three-dimensional laser scanned scenes to be inadequate. The similarity metric used to score the registration creates a number of local minima that inhibits searching via Powell's Multidimensional Minimization Algorithm, a gradient-descent technique. To find the best metric for general environment scanning, we examine the results of different information-theoretic metrics. Our examination leads us to the conclusion that gradient-descent based techniques are not a good choice for unsupervised automatic registration for images from environment scans. However an unsupervised process is possible through global-optimization techniques at the cost of longer processing times.