SIFT, SURF and seasons : long-term outdoor localization using local features
Year: 2007
Type: conference-paper
Abstract: Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. In this paper, we address the issues of outdoor appearance-based topological localization for a mobile robot over time. Our data sets, each consisting of a large number of panoramic images, have been acquired over a period of nine months with large seasonal changes (snowcovered ground, bare trees, autumn leaves, dense foliage, etc.). Two different types of image feature algorithms, SIFT and the more recent SURF, have been used to compare the images. We show that two variants of SURF, called U-SURF and SURF-128, outperform the other algorithms in terms of accuracy and speed.
Authors: Christoffer Valgren, Achim J. Lilienthal
Institution: Örebro University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1488482743