Krajnik, Tomas and Cristoforis, Pablo and Kusumam, Keerthy and Neubert, Peer and Duckett, Tom (2017) Image features for visual teach-and-repeat navigation in changing environments. Robotics and Autonomous Systems, 88 . pp. 127-141. ISSN 0921-8890
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|Item Status:||Live Archive|
We present an evaluation of standard image features in the context of long-term visual teach-and-repeat navigation of mobile robots, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes. We argue that for long-term autonomous navigation, the viewpoint-, scale- and rotation- invariance of the standard feature extractors is less important than their robustness to the mid- and long-term environment appearance changes. Therefore, we focus our evaluation on the robustness of image registration to variable lighting and naturally-occurring seasonal changes. We combine detection and description components of different image extractors and evaluate their performance on five datasets collected by mobile vehicles in three different outdoor environments over the course of one year. Moreover, we propose a trainable feature descriptor based on a combination of evolutionary algorithms and Binary Robust Independent Elementary Features, which we call GRIEF (Generated BRIEF). In terms of robustness to seasonal changes, the most promising results were achieved by the SpG/CNN and the STAR/GRIEF feature, which was slightly less robust, but faster to calculate.
|Keywords:||Visual Navigation, Mobile robotics, Long-term autonomy|
|Subjects:||H Engineering > H670 Robotics and Cybernetics|
G Mathematical and Computer Sciences > G740 Computer Vision
|Divisions:||College of Science > School of Computer Science|
|Deposited On:||27 Nov 2016 11:59|
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