Abstract
Appearance-based localization attempts to recover the position (and orientation) of a camera based on the images that it captured and a previously stored collection of images. Recent advances in image representations extracted using convolutional neural networks for the task of place recognition have produced whole-image descriptors which are robust to imaging conditions, including small viewpoint changes. In previous work, we have used these descriptors to perform localization by performing descriptor interpolation to compare the appearance of the image that is currently captured with the expected appearance at a candidate location. In this work, we directly study the behaviour of recently developed whole-image descriptors for this application.
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Acknowledgments
This work has been supported by the Spanish Government, the “European Regional Development Fund ERDF” under contract DPI2014-55826-R, and the EU-H2020 project MOVECARE.
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Lopez-Antequera, M., Gonzalez-Jimenez, J., Petkov, N. (2018). Evaluation of Whole-Image Descriptors for Metric Localization. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10672. Springer, Cham. https://doi.org/10.1007/978-3-319-74727-9_33
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DOI: https://doi.org/10.1007/978-3-319-74727-9_33
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