An Extensive Approach to Features Detection and Description for 2-D Range Data Using Active B-splines

Abstract

This letter presents three novel contributions for the two-dimensional laser scan data. First, a low-level point feature detector for stable interest point detection. Second, an approach for high-level B-splines features extraction, and finally, a local descriptor for the interest points based on the shape context representation of the uniformly distributed points on the B-splines features. The clues of stable interest points obtained from the detector are used in the B-splines features extraction from the scan data, which enables lightweight and accurate mapping of the environment using fewer control points instead of scan data points. This letter also proposes benchmark metrics to evaluate the performance of the extracted high-level features approach in comparison with the state-of-the-art methodology. A robust descriptor for the interest points offers an efficient association and correspondence matching. Consistent results compared with the state-of-the-art FALKO-BSC and FLIRT-beta-Grid features in experiments with standard datasets prove the stability of our proposed interest point detector and the proficiency of the descriptor. The results of efficient localization using the pair are also presented.