Appearance-Based Sequential Robot Localization Using a Patchwise Approximation of a Descriptor Manifold
This paper addresses appearance-based robot localization in 2D with a sparse, lightweight map of the environment composed of descriptor-pose image pairs. Based on previous research in the field, we assume that image descriptors are samples of a low-dimensional Descriptor Manifold that is locally art...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Instituto de Salud Carlos III (ISCIII) |
| Repositorio: | Repisalud |
| Idioma: | inglés |
| OAI Identifier: | oai:repisalud.isciii.es:20.500.12105/18304 |
| Acceso en línea: | http://hdl.handle.net/20.500.12105/18304 |
| Access Level: | acceso abierto |
| Palabra clave: | Appearance-based localization Computer vision Gaussian processes Manifold learning Robot vision systems Indoor positioning Image manifold Descriptor manifold Aprendizaje Descriptores Reconocimiento de normas patrones automatizadas Ambiente Métodos Inteligencia artificial Lighting Pattern Recognition, Automated Imaging, Three-Dimensional Image Interpretation, Computer-Assisted Uncertainty Environment Normal Distribution Artificial Intelligence |
| Sumario: | This paper addresses appearance-based robot localization in 2D with a sparse, lightweight map of the environment composed of descriptor-pose image pairs. Based on previous research in the field, we assume that image descriptors are samples of a low-dimensional Descriptor Manifold that is locally articulated by the camera pose. We propose a piecewise approximation of the geometry of such Descriptor Manifold through a tessellation of so-called Patches of Smooth Appearance Change (PSACs), which defines our appearance map. Upon this map, the presented robot localization method applies both a Gaussian Process Particle Filter (GPPF) to perform camera tracking and a Place Recognition (PR) technique for relocalization within the most likely PSACs according to the observed descriptor. A specific Gaussian Process (GP) is trained for each PSAC to regress a Gaussian distribution over the descriptor for any particle pose lying within that PSAC. The evaluation of the observed descriptor in this distribution gives us a likelihood, which is used as the weight for the particle. Besides, we model the impact of appearance variations on image descriptors as a white noise distribution within the GP formulation, ensuring adequate operation under lighting and scene appearance changes with respect to the conditions in which the map was constructed. A series of experiments with both real and synthetic images show that our method outperforms state-of-the-art appearance-based localization methods in terms of robustness and accuracy, with median errors below 0.3 m and 6°. |
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