Uma Abordagem Baseada em Visão Computacional para Localização e Mapeamento Simultâneos de Robôs Subaquáticos
Resumo
O uso de veículos subaquáticos autônomos para inspeção visual é um campo promissor da robótica. Devido à dificuldade de localizar o robô e mapear o ambiente simultaneamente (SLAM), este trabalho propõe o uso de visão computacional e de mapas topológicos. Por meio de uma câmera de inspeção como fonte sensorial, essa abordagem é composta por dois estágios principais: i) aplicação do SIFT para extração de características em seqüencias de imagens e ii) uso de mapas auto-organizáveis. O sistema desenvolvido foi validado em situações reais e simuladas, usando robôs reais em testes online. A precisão e a robustez obtidas em condições subaquáticas desfavoráveis, como variação de iluminação e ruído, conduzem a uma original e eficiente técnica de SLAM.Referências
Arredondo, M. and Lebart, K. (2005). A methodology for the systematic assessment of underwater video processing algorithms. In IEEE/OES Oceans, volume 1, pages 362–367.
Birchfield, S. (2007). KLT 1.3.4 : A C implementation of KLT tracker. [link]. [Acessado em 12 de Março de 2008.].
Booij, O., Terwijn, B., Zivkovic, Z., and Krose, B. (2007). Navigation using an appearance based topological map. In IEEE ICRA, pages 3927–3932.
Fritzke, B. (1993). Growing cell structures - a self-organizing network for unsupervised and supervised learning. Technical report, University of California.
Garcia, R. (2001). A Proposal to Estimate the Motion of an Underwater Vehicle Through Visual Mosaicking. PhD thesis, Universitat de Girona.
Gracias, N., Van der Zwaan, S., Bernardino, A., and Santos-Vitor, J. (2002). Results on underwater mosaic-based navigation. In MTS/IEEE Oceans, volume 3, pages 1588–1594.
Kohonen, T. (2001). Self-Organizing Maps. Springer-Verlag New York, Inc., Secaucus, NJ, USA.
Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110.
Mahon, I. and Williams, S. (2004). SLAM using natural features in an underwater environment. In IEEE ICARCV, volume 3, pages 2076–2081.
Marks, R., Rock, S., and Lee, M. (1995). Real-time video mosaicking of the ocean floor. IEEE Journal of Oceanic Engineering, 20(3):229–241.
McFarlane, J. R. (2000). Underwater technology 2000 rovs and auvs: tools for exploring, exploiting and defending the ocean frontier. Underwater Technology, pages 465–471.
Negahdaripour, S. and Khamene, A. (2000). Motion-based compression of underwater video imagery for the operations of unmanned submersible vehicles. Computer Vision and Image Understanding, 79(1):162–183.
Plakas, K. and Trucco, E. (2000). Developing a real-time, robust, video tracker. In MTS/IEEE Oceans, volume 2, pages 1345–1352.
Se, S., Lowe, D., and Little, J. (2005). Vision-based global localization and mapping for mobile robots. IEEE Transactions on Robotics, 21(3):364–375.
Shi, J. and Tomasi, C. (1994). Good features to track. In IEEE Conference on Computer Vision and Pattern Recognition, pages 593–600.
Stewart, C. V. (1999). Robust parameter estimation in computer vision. Society for Industrial and Applied Mathematics Review, 41(3):513–537.
Xu, X. and Negahdaripour, S. (1997). Vision-based motion sensing for underwater navigation and mosaicing of ocean floor images. In MTS/IEEE Oceans, volume 2, pages 1412–1417.
Birchfield, S. (2007). KLT 1.3.4 : A C implementation of KLT tracker. [link]. [Acessado em 12 de Março de 2008.].
Booij, O., Terwijn, B., Zivkovic, Z., and Krose, B. (2007). Navigation using an appearance based topological map. In IEEE ICRA, pages 3927–3932.
Fritzke, B. (1993). Growing cell structures - a self-organizing network for unsupervised and supervised learning. Technical report, University of California.
Garcia, R. (2001). A Proposal to Estimate the Motion of an Underwater Vehicle Through Visual Mosaicking. PhD thesis, Universitat de Girona.
Gracias, N., Van der Zwaan, S., Bernardino, A., and Santos-Vitor, J. (2002). Results on underwater mosaic-based navigation. In MTS/IEEE Oceans, volume 3, pages 1588–1594.
Kohonen, T. (2001). Self-Organizing Maps. Springer-Verlag New York, Inc., Secaucus, NJ, USA.
Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2):91–110.
Mahon, I. and Williams, S. (2004). SLAM using natural features in an underwater environment. In IEEE ICARCV, volume 3, pages 2076–2081.
Marks, R., Rock, S., and Lee, M. (1995). Real-time video mosaicking of the ocean floor. IEEE Journal of Oceanic Engineering, 20(3):229–241.
McFarlane, J. R. (2000). Underwater technology 2000 rovs and auvs: tools for exploring, exploiting and defending the ocean frontier. Underwater Technology, pages 465–471.
Negahdaripour, S. and Khamene, A. (2000). Motion-based compression of underwater video imagery for the operations of unmanned submersible vehicles. Computer Vision and Image Understanding, 79(1):162–183.
Plakas, K. and Trucco, E. (2000). Developing a real-time, robust, video tracker. In MTS/IEEE Oceans, volume 2, pages 1345–1352.
Se, S., Lowe, D., and Little, J. (2005). Vision-based global localization and mapping for mobile robots. IEEE Transactions on Robotics, 21(3):364–375.
Shi, J. and Tomasi, C. (1994). Good features to track. In IEEE Conference on Computer Vision and Pattern Recognition, pages 593–600.
Stewart, C. V. (1999). Robust parameter estimation in computer vision. Society for Industrial and Applied Mathematics Review, 41(3):513–537.
Xu, X. and Negahdaripour, S. (1997). Vision-based motion sensing for underwater navigation and mosaicing of ocean floor images. In MTS/IEEE Oceans, volume 2, pages 1412–1417.
Publicado
12/07/2008
Como Citar
DREWS JR, Paulo; BOTELHO, Silvia.
Uma Abordagem Baseada em Visão Computacional para Localização e Mapeamento Simultâneos de Robôs Subaquáticos. In: CONCURSO DE TRABALHOS DE INICIAÇÃO CIENTÍFICA DA SBC (CTIC-SBC), 27. , 2008, Belém/PA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2008
.
p. 81-90.
