Um Método para Localização de Veículos Subaquáticos Baseado em Imagens Visíveis Aéreas e Acústicas Subaquáticas
Resumo
Este artigo apresenta um framework multi domínio e multi perspectiva para localização de robôs submarinos, que não requer nenhuma informação do Sistema de Posicionamento Global (GPS). O método de localização proposto usa imagens aéreas coloridas e imagens acústicas subaquáticas para estimar a posição do robô. O método identifica a correlação entre imagens de domínios distintos, dada pelo casamento de imagens adquiridas em ambientes parcialmente estruturados com características compartilhadas. A validação do método proposto é feita usando um conjunto de dados real, que foi adquirido por um veículo submarino em uma marina. Além disso, foi realizada a comparação com Dead Reckoning e um método baseado em aprendizado. Os resultados experimentais apresentam a viabilidade do método proposto e seus avanços em relação ao estado da arte.
Referências
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Drews-Jr, P., Nascimento, E. R., Campos, M. F. M., and Elfes, A. (2015). Automatic restoration of underwater monocular sequences of images. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1058-1064.
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Hu, S., Feng, M., Nguyen, R. M. H., and Hee Lee, G. (2018). CVM-Net: Cross-view matching network for image-based ground-to-aerial geo-localization. In IEEE CVPR, pages 7258-7267.
Hurtos, N., Nagappa, S., Cufi, X., Petillot, Y., and Salvi, J. (2013). Evaluation of registration methods on two-dimensional forward-looking sonar imagery. In OCEANS Bergen, 2013 MTS/IEEE, pages 1-8.
Leung, K. Y. K., Clark, C. M., and Huissoon, J. P. (2008). Localization in urban environments by matching ground level video images with an aerial image. In IEEE ICRA 2008, pages 551-556.
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Thrun, S. (2002). Probabilistic robotics. Communications of the ACM, 45(3):52-57.
Tian, Y., Chen, C., and Shah, M. (2017). Cross-view image matching for geo-localization in urban environments. In IEEE CVPR, pages 3608-3616.
Wolff, M., Collins, R. T., and Liu, Y. (2016). Regularity-driven building facade matching between aerial and street views. In IEEE CVPR, pages 1591-1600.
Workman, S. and Jacobs, N. (2015). On the location dependence of convolutional neural network features. In IEEE CVPRw, pages 70-78.
Workman, S., Souvenir, R., and Jacobs, N. (2015). Wide-area image geolocalization with aerial reference imagery. In The IEEE International Conference on Computer Vision (ICCV).
Wu, Y. (2019). Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV) and an autonomous underwater vehicle (AUV) in an underwater target strike mission. Ocean Engineering, 182:162 - 173.
Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V. (2008). Speeded-up robust features (surf). CVIU, 110(3):346 - 359.
C. S. Ribeiro, P. O., M. dos Santos, M., L. J. Drews-Jr, P., and S. C. Botelho, S. (2017). Forward looking sonar scene matching using deep learning. In 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pages 574-579.
Castaldo, F., Zamir, A., Angst, R., Palmieri, F., and Savarese, S. (2015). Semantic crossview matching. In IEEE ICCVw, pages 9-17.
Chopra, S., Hadsell, R., and LeCun, Y. (2005). Learning a similarity metric discriminatively, with application to face verification. In IEEE CVPR, volume 1, pages 539-546.
Concha, A., Drews-Jr, P., Campos, M., and Civera, J. (2015). Real-time localization and dense mapping in underwater environments from a monocular sequence. In OCEANS 2015 Genova, pages 1-5.
De Giacomo, G. G., dos Santos, M. M., Drews-Jr, P. L., and Botelho, S. S. (2020). Cooperative training of triplet networks for cross-domain matching. In 2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Education (WRE), pages 1-6. IEEE.
Djuric, P. M., Kotecha, J. H., Zhang, J., Huang, Y., Ghirmai, T., Bugallo, M. F., and Miguez, J. (2003). Particle filtering. IEEE Signal Processing Magazine, 20(5):19-38.
Dos Santos, M. M., De Giacomo, G. G., Drews-Jr, P. L., and Botelho, S. S. (2022). Crossview and cross-domain underwater localization based on optical aerial and acoustic underwater images. IEEE Robotics and Automation Letters, 7(2):4969-4974.
Dos Santos, M. M., De Giacomo, G. G., Drews-Jr, P. L., Botelho, S. S., and Mello, C. D. (2021). A framework for underwater vehicle localization based on cross-view and cross-domain acoustic and aerial images. In 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian Symposium on Robotics (SBR), and 2021 Workshop on Robotics in Education (WRE), pages 204-209. IEEE.
Drews-Jr, P., Nascimento, E. R., Campos, M. F. M., and Elfes, A. (2015). Automatic restoration of underwater monocular sequences of images. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1058-1064.
Gao, X., Shen, S., Hu, Z., and Wang, Z. (2018). Ground and aerial meta-data integration for localization and reconstruction: A review. Pattern Recognition Letters, 127:202-214.
Hu, S., Feng, M., Nguyen, R. M. H., and Hee Lee, G. (2018). CVM-Net: Cross-view matching network for image-based ground-to-aerial geo-localization. In IEEE CVPR, pages 7258-7267.
Hurtos, N., Nagappa, S., Cufi, X., Petillot, Y., and Salvi, J. (2013). Evaluation of registration methods on two-dimensional forward-looking sonar imagery. In OCEANS Bergen, 2013 MTS/IEEE, pages 1-8.
Leung, K. Y. K., Clark, C. M., and Huissoon, J. P. (2008). Localization in urban environments by matching ground level video images with an aerial image. In IEEE ICRA 2008, pages 551-556.
Lin, T.-Y., Belongie, S., and Hays, J. (2013). Cross-view image geolocalization. In IEEE CVPR, pages 891-898.
Lin, T.-Y., Cui, Y., Belongie, S., and Hays, J. (2015). Learning deep representations for ground-to-aerial geolocalization. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Matas, J., Galambos, C., and Kittler, J. (2000). Robust detection of lines using the progressive probabilistic hough transform. CVIU, 78(1):119-137.
Noda, M., Takahashi, T., Deguchi, D., Ide, I., Murase, H., Kojima, Y., and Naito, T. (2010). Vehicle ego-localization by matching in-vehicle camera images to an aerial image. In ACCV, pages 163-173.
Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., Wheeler, R., and Ng, A. Y. (2009). Ros: an open-source robot operating system. In ICRA workshop on open source software, volume 3, page 5. Kobe, Japan.
Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., and Garnett, R., editors, Advances in Neural Information Processing Systems, volume 28. Curran Associates, Inc.
Thrun, S. (2002). Probabilistic robotics. Communications of the ACM, 45(3):52-57.
Tian, Y., Chen, C., and Shah, M. (2017). Cross-view image matching for geo-localization in urban environments. In IEEE CVPR, pages 3608-3616.
Wolff, M., Collins, R. T., and Liu, Y. (2016). Regularity-driven building facade matching between aerial and street views. In IEEE CVPR, pages 1591-1600.
Workman, S. and Jacobs, N. (2015). On the location dependence of convolutional neural network features. In IEEE CVPRw, pages 70-78.
Workman, S., Souvenir, R., and Jacobs, N. (2015). Wide-area image geolocalization with aerial reference imagery. In The IEEE International Conference on Computer Vision (ICCV).
Wu, Y. (2019). Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV) and an autonomous underwater vehicle (AUV) in an underwater target strike mission. Ocean Engineering, 182:162 - 173.
Publicado
18/10/2022
Como Citar
SANTOS, Matheus Machado dos; DREWS-JR, Paulo Lilles Jorge; BOTELHO, Silvia Silva da Costa.
Um Método para Localização de Veículos Subaquáticos Baseado em Imagens Visíveis Aéreas e Acústicas Subaquáticas. In: CONCURSO DE TESES E DISSERTAÇÕES EM ROBÓTICA - CTDR (DOUTORADO) - SIMPÓSIO BRASILEIRO DE ROBÓTICA E SIMPÓSIO LATINO-AMERICANO DE ROBÓTICA (SBR/LARS), 14. , 2022, São Bernardo do Campo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 133-144.
DOI: https://doi.org/10.5753/wtdr_ctdr.2022.227387.
