Congruence Transformation Invariant Feature Descriptor for Robust 2D Scan Matching
Takayuki Nakamura, Yuuichi Tashita · 2013
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.