The Scale Invariant Feature Transform, SIFT, has been successfully applied to robot localization. Still, the number of features extracted with this approach is immense, especially when dealing with omnidirectional vision. In this work, we propose a new approach that reduces the number of features generated by SIFT as well as their extraction and matching time. With the help of a particle filter, we demonstrate that we can still localize the mobile robot accurately with a lower number of features.
A Scale Invariant Feature Transform (SIFT) is proposed in REF for robot localization that decreases the sum features generated by SIFT as well as their time of matching and extraction.
Localization of Mobile Robots with Omnidirectional Vision using Particle Filter and Iterative SIFT
In Proceedings of the 2005 European Conference on Mobile Robots (ECMR05
Computer Science