Identificação e localização dos limites da região trafegável para a navegação de um veículo autônomo
This work presents a computer vision system for real-time mapping of the traversable region ahead of an autonomous vehicle that relies mostly on a stereo camera. For each new image pair captured by the stereo camera, the system first determines the camera position with re-spect to the ground plane u...
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| Tipo de recurso: | tesis de maestría |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Brasil |
| Institución: | Universidade Federal do Espírito Santo (UFES) |
| Repositorio: | Repositório Institucional da Universidade Federal do Espírito Santo (riUfes) |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufes.br:10/6425 |
| Acceso en línea: | http://repositorio.ufes.br/handle/10/6425 |
| Access Level: | acceso abierto |
| Palabra clave: | Visão artificial Robótica Navegação de robôs móveis Aprendizado do computador Ciência da Computação 004 |
| Sumario: | This work presents a computer vision system for real-time mapping of the traversable region ahead of an autonomous vehicle that relies mostly on a stereo camera. For each new image pair captured by the stereo camera, the system first determines the camera position with re-spect to the ground plane using stereo vision algorithms and probabilistic methods, and then projects the camera raw image to the world coordinate system using inverse perspective map-ping. After that, the system classifies the pixels of the inverse perspective map as traversable or not traversable, generating an instantaneous occupancy map. Next, the instantaneous occu-pancy map is integrated to a probabilistic occupancy map, using the Bayes filter to estimate the occupancy probability of each map location. Finally, the probabilistic occupancy map is used to localize the lateral limits of the traversable region. The performance of the system for mapping the traversable region was evaluated in compari-son to manually classified images. The experimental results show that the system is able to correctly map up to 92.22 % of the traversable locations between 20 and 35 m ahead of an autonomous vehicle with a False Acceptance Rate not superior to 3.57 % when mapping par-allelepiped pavements. |
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