2026-06-14T03:42:04Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/183042024-11-28T14:52:25Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16927
Repisalud author Jaenal, Alberto author Moreno, Francisco-Angel author Gonzalez-Jimenez, Javier authoraffiliation [Jaenal,A; Moreno,FA; Gonzalez-Jimenez,J] Machine Perception and Intelligent Robotics Group (MAPIR), Department of System Engineering and Automation Biomedical Research Institute of Malaga (IBIMA), University of Malaga, Málaga, Spain. 2024-02-19T15:26:46Z 2024-02-19T15:26:46Z 2021-04-02 10.3390/s21072483 1424-8220 Sensors http://hdl.handle.net/10668/3818 33918493 http://hdl.handle.net/20.500.12105/18304 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°. eng 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 Appearance-Based Sequential Robot Localization Using a Patchwise Approximation of a Descriptor Manifold research article