Maximum likelihood mapping with spectral image registration

Abstract— A core challenge in probabilistic mapping is to extract meaningful uncertainty information from data registration methods. While this has been investigated in ICP-based scan matching methods, other registration methods have not been analyzed. In this paper, an uncertainty analysis of a Fourier Mellin based image registration algorithm is introduced, which to our knowledge is the first of its kind involving spectral registration. A covariance matrix is extracted from the result of a Phase-Only Matched Filter, which is interpreted as a probability mass function. The method is embedded in a pose graph implementation for Simultaneous Localization and Mapping (SLAM) and validated with experiments in the underwater domain. published in: International Conference on Robotics and Automation (ICRA), IEEE Press, 2010

• Type: Journal
• Year: 2010
• Where: ICRA
• Authors: Max Pfingsthorn, Andreas Birk 0002, Sören Schwertfeger, Heikow Bülow, Kaustubh Pathak
• Keywords: Data Registration Methods, ICRA 2010, Registration Methods, Robotics, Scan Matching Methods