Abstract
The ability of computing similarities between two data sets is a key for many applications such as video tracking, object recognition, image stitching, 3D modeling and so on. Recently, Lowe has discovered a promissing approach for matching 2D images based on the local invariant feature descriptor called SIFT [1]. We are really inspired by Lowe's method. In this paper, we propose a new local invariant feature descriptor for matching 2D scan data. The proposed feature descriptor is called CIF, that is a feature which remains unchanged when a congruence transformation is applied. We can perform global scan matching in cluttered environments by matching an input scan with a reference scan based on CIF without any initial alignments. the validity of our method is confirmed by experiments in real environment.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.