ROS2D: Image feature detector using rank order statistics
Khalid Yousif, Yuichi Taguchi, Srikumar Ramalingam, Alireza Bab-Hadiashar
Abstract
We present a new image feature detection method. Our method selects features based on segmenting points with high local intensity variations across different scales using a robust rank order statistics approach. Our method produces a large number of repeatable features that are invariant to several image transformations such as rotation, scaling, viewpoint, and lighting variations. We show the advantages of our feature in comparison to other existing features using the Oxford dataset. We also show that, when used in monocular and stereo SLAM systems, our feature outperforms SIFT in terms of the pose estimation accuracy using several public datasets including the KITTI dataset.
BibTeX
@inproceedings{icra2017_ros2dimagefeatur,
title = {ROS2D: Image feature detector using rank order statistics},
author = {Khalid Yousif and Yuichi Taguchi and Srikumar Ramalingam and Alireza Bab-Hadiashar},
booktitle = {ICRA 2017},
year = {2017}
}