@inproceedings{a01fdee5c20f4ac1be94a44768a5bb89,
title = "Using Local Symmetry for Landmark Selection",
abstract = "Most visual Simultaneous Localization And Mapping (SLAM) methods use interest points as landmarks in their maps of the environment. Often the interest points are detected using contrast features, for instance those of the Scale Invariant Feature Transform (SIFT). The SIFT interest points, however, have problems with stability, and noise robustness. Taking our inspiration from human vision, we therefore propose the use of local symmetry to select interest points, Our method, the MUlti-scale Symmetry Transform (MUST), was tested on a robot-generated database including ground-truth information to quantify SLAM performance. We show that interest points selected using symmetry are more robust to noise and contrast manipulations, have a slightly better repeatability, and above all, result in better overall SLAM performance.",
keywords = "ROBOT LOCALIZATION, ACTIVE-VISION, FEATURES, DETECTORS, ATTENTION, OBJECTS, MODEL, SURF",
author = "Gert Kootstra and {de Jong}, Sjoerd and Schomaker, {Lambert R. B.}",
year = "2009",
language = "English",
isbn = "978-3-642-04666-7",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "94--103",
editor = "M Fritz and B Schiele and JH Piater",
booktitle = "COMPUTER VISION SYSTEMS, PROCEEDINGS",
note = "7th International Conference on Computer Vision Systems ; Conference date: 13-10-2009 Through 15-10-2009",
}