Monte Carlo Localization of Mobile Robot with Modified SIFT

Abstract

The scale invariant feature transform, SIFT, is invariant to image translation, scaling, rotation, and is partially invariant to illumination changes. But, the time of features extraction and matching is huge, and the number of features is much larger then that is needed. To reduce the number of features generated by SIFT as well as their extraction and matching time, a modified approach based sampling is proposed. Mean-Shift algorithm is used in this modified SIFT to search local extrema points actively in scale space to improve the efficiency. The modified SIFT is used in Monte Carlo localization of mobile robots with omnidirectional sensor, it is demonstrated that the features extracted by modified SIFT are uniformly distributed in space, the time of feature extraction and matching is reduced obviously, and the mobile robots can localize itself accurately with a lower number of features.