Towards training-free appearance-based localization : probabilistic models for whole-image descriptors
Lowry, Stephanie, Wyeth, Gordon, & Milford, Michael (2014) Towards training-free appearance-based localization : probabilistic models for whole-image descriptors. In Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA 2014), IEEE, Hong Kong Convention and Exhibition Center, Hong Kong, pp. 711-717.
Whole image descriptors have recently been shown to be remarkably robust to perceptual change especially compared to local features. However, whole-image-based localization systems typically rely on heuristic methods for determining appropriate matching thresholds in a particular environment. These environment-specific tuning requirements and the lack of a meaningful interpretation of these arbitrary thresholds limits the general applicability of these systems. In this paper we present a Bayesian model of probability for whole-image descriptors that can be seamlessly integrated into localization systems designed for probabilistic visual input. We demonstrate this method using CAT-Graph, an appearance-based visual localization system originally designed for a FAB-MAP-style probabilistic input. We show that using whole-image descriptors as visual input extends CAT-Graph’s functionality to environments that experience a greater amount of perceptual change. We also present a method of estimating whole-image probability models in an online manner, removing the need for a prior training phase. We show that this online, automated training method can perform comparably to pre-trained, manually tuned local descriptor methods.
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|Item Type:||Conference Paper|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
|Copyright Owner:||Copyright 2014 IEEE|
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|Deposited On:||09 Mar 2014 23:06|
|Last Modified:||28 Oct 2014 14:23|
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