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.
REF propose an approach that reduces the number of features generated by SIFT, and with the help of a particle filter, the robot location can still be tracked accurately.
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