Abstract
Images of a given environment, coded by a holistic image descriptor, produce a manifold that is articulated by the camera pose in such environment. The correct articulation of such Descriptor Manifold (DM) by the camera poses is the cornerstone for precise Appearance-based Localization (AbL), which implies knowing the correspondent descriptor for any given pose of the camera in the environment. Since such correspondences are only given at sample pairs of the DM (the appearance map), some kind of regression must be applied to predict descriptor values at unmapped locations. This is relevant for AbL because this regression process can be exploited as an observation model for the localization task. This paper analyses the influence of a number of parameters involved in the approximation of the DM from the appearance map, including the sampling density, the method employed to regress values at unvisited poses, and the impact of the image content on the DM structure. We present experimental evaluations of diverse setups and propose an image metric based on the image derivatives, which allows us to build appearance maps in the form of grids of variable density. A preliminary use case is presented as an initial step for future research.
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Acknowledgements
This research was funded by: Government of Spain grant number FPU17/04512; and under projects ARPEGGIO (PID2020-117057) and WISER (DPI2017-84827-R) financed by the Government of Spain and European Regional Development’s funds (FEDER). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal used for this research.
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Jaenal, A., Moreno, FA., Gonzalez-Jimenez, J. (2021). Experimental Analysis of Appearance Maps as Descriptor Manifolds Approximations. In: Tsapatsoulis, N., Panayides, A., Theocharides, T., Lanitis, A., Pattichis, C., Vento, M. (eds) Computer Analysis of Images and Patterns. CAIP 2021. Lecture Notes in Computer Science(), vol 13053. Springer, Cham. https://doi.org/10.1007/978-3-030-89131-2_10
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