Use este identificador para citar ou linkar para este item:
http://repositorio.ufla.br/jspui/handle/1/49151
Título: | Soft constrained autonomous vehicle navigation using gaussian processes and instance segmentation |
Palavras-chave: | Map-based Localization Monocular Vision Instance Segmentation Gaussian Process Constrained Particle Filter Veículos autônomos Visão Monocular Segmentação de instância Processo Gaussiano |
Data do documento: | Jan-2021 |
Editor: | Cornell University |
Citação: | BARBOSA, B. H. G. et al. Soft constrained autonomous vehicle navigation using gaussian processes and instance segmentation. ArXiv, [S.I.], 2021. DOI: arxiv-2101.06901. |
Resumo: | This paper presents a generic feature-based navigation framework for autonomous vehicles using a soft constrained Particle Filter. Selected map features, such as road and landmark locations, and vehicle states are used for designing soft constraints. After obtaining features of mapped landmarks in instance-based segmented images acquired from a monocular camera, vehicle-to-landmark distances are predicted using Gaussian Process Regression (GPR) models in a mixture of experts approach. Both mean and variance outputs of GPR models are used for implementing adaptive constraints. Experimental results confirm that the use of image segmentation features improves the vehicle-to-landmark distance prediction notably, and that the proposed soft constrained approach reliably localizes the vehicle even with reduced number of landmarks and noisy observations. |
URI: | https://arxiv.org/abs/2101.06901 http://repositorio.ufla.br/jspui/handle/1/49151 |
Aparece nas coleções: | DEG - Artigos publicados em periódicos |
Arquivos associados a este item:
Não existem arquivos associados a este item.
Os itens no repositório estão protegidos por copyright, com todos os direitos reservados, salvo quando é indicado o contrário.