Please use this identifier to cite or link to this item:http://hdl.handle.net/20.500.12105/18304
Title
Appearance-Based Sequential Robot Localization Using a Patchwise Approximation of a Descriptor Manifold
Author(s)
Date issued
2021-04-02
Language
Inglés
Document type
research article
Abstract
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°.
Subject
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
MESH
Lighting | Pattern Recognition, Automated | Imaging, Three-Dimensional | Image Interpretation, Computer-Assisted | Uncertainty | Environment | Normal Distribution | Artificial Intelligence
Online version
DOI
Collections
Full text access