- -

WGANVO: odometría visual monocular basada en redes adversarias generativas

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

WGANVO: odometría visual monocular basada en redes adversarias generativas

Mostrar el registro completo del ítem

Cremona, J.; Uzal, L.; Pire, T. (2022). WGANVO: odometría visual monocular basada en redes adversarias generativas. Revista Iberoamericana de Automática e Informática industrial. 19(2):144-153. https://doi.org/10.4995/riai.2022.16113

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/182820

Ficheros en el ítem

Metadatos del ítem

Título: WGANVO: odometría visual monocular basada en redes adversarias generativas
Otro titulo: WGANVO: monocular visual odometry based on generative adversarial networks
Autor: Cremona, Javier Uzal, Lucas Pire, Taihú
Fecha difusión:
Resumen:
[EN] Traditional Visual Odometry (VO) systems, direct or feature-based, are susceptible to matching errors between images. Furthermore, monocular configurations are only capable of estimating localization up to a scale ...[+]


[ES] Los sistemas tradicionales de odometría visual (VO), directos o basados en características visuales, son susceptibles de cometer errores de correspondencia entre imágenes. Además, las configuraciones monoculares sólo ...[+]
Palabras clave: Localization , Neural networks , Mobile robots , Localización , Redes Neuronales , Robots Móviles
Derechos de uso: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
Fuente:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.4995/riai.2022.16113
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/riai.2022.16113
Código del Proyecto:
info:eu-repo/grantAgreement/CONICET-UNR//PUE 0015-2016
Agradecimientos:
Este trabajo fue financiado por el CIFASIS, Centro Franco Argentino de Ciencias de la Información y de Sistemas (CONICET-UNR), con el proyecto de Unidad Ejecutora PUE 0015-2016.
Tipo: Artículo

References

Agrawal, P., Carreira, J., Malik, J., 2015. Learning to See by Moving. In: Proceedings of the International Conference on Computer Vision. pp. 37-45. https://doi.org/10.1109/ICCV.2015.13

Almalioglu, Y., Saputra, M. R. U., de Gusmao, P. P. B., Markham, A., Trigoni, N., 2019. GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks. In: Proceedings of the IEEE International Conference on Robotics and Automation. pp. 5474-5480. https://doi.org/10.1109/ICRA.2019.8793512

Comport, A. I., Malis, E., Rives, P., 2010. Real-time quadrifocal visual odometry. International Journal of Robotics Research, 245-266. https://doi.org/10.1177/0278364909356601 [+]
Agrawal, P., Carreira, J., Malik, J., 2015. Learning to See by Moving. In: Proceedings of the International Conference on Computer Vision. pp. 37-45. https://doi.org/10.1109/ICCV.2015.13

Almalioglu, Y., Saputra, M. R. U., de Gusmao, P. P. B., Markham, A., Trigoni, N., 2019. GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks. In: Proceedings of the IEEE International Conference on Robotics and Automation. pp. 5474-5480. https://doi.org/10.1109/ICRA.2019.8793512

Comport, A. I., Malis, E., Rives, P., 2010. Real-time quadrifocal visual odometry. International Journal of Robotics Research, 245-266. https://doi.org/10.1177/0278364909356601

Cremona, J., Uzal, L., Pire, T., 2021. WGANVO Repository.https://github.com/CIFASIS/wganvo, [Online; accessed 19-August-2021].

Engel, J., Agrawal, K. K., Chen, S., Gulrajani, I., Donahue, C., Roberts, A.,2019. GANSynth: Adversarial Neural Audio Synthesis. In: Proceedings of the International Conference on Learning Representations. URL: https://openreview.net/pdf?id=H1xQVn09FX

Engel, J., Koltun, V., Cremers, D., 2018. Direct Sparse Odometry. IEEE Transactions on Pattern Analysis and Machine Intelligence, 611-625. https://doi.org/10.1109/TPAMI.2017.2658577

Engel, J., Schöps, T., Cremers, D., 2014. LSD-SLAM: Large-Scale Direct Monocular SLAM. In: Proceedings of the European Conference on Computer Vision. pp. 834-849. https://doi.org/10.1007/978-3-319-10605-2_54

Facil, J. M., Ummenhofer, B., Zhou, H., Montesano, L., Brox, T., Civera, J.,2019. CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 11818-11827. https://doi.org/10.1109/CVPR.2019.01210

Forster, C., Pizzoli, M., Scaramuzza, D., 2014. SVO: Fast semi-direct monocular visual odometry. In: Proceedings of the IEEE International Conference on Robotics and Automation. pp. 15-22. https://doi.org/10.1109/ICRA.2014.6906584

Geiger, A., Lenz, P., Stiller, C., Urtasun, R., 2013. Vision Meets Robotics: The KITTI Dataset. International Journal of Robotics Research, 1231-1237. https://doi.org/10.1177/0278364913491297

Geiger, A., Lenz, P., Urtasun, R., 2012. Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3354-3361. https://doi.org/10.1109/CVPR.2012.6248074

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair,S., Courville, A., Bengio, Y., 2014. Generative adversarial nets. In: Proceedings of the Advances in Neural Information Processing Systems. pp. 2672-2680.

Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A. C., 2017. Improved Training of Wasserstein GANs. In: Guyon, I., Luxburg, U. V.,Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (Eds.),Proceedings of the Advances in Neural Information Processing Systems.Vol. 30. Curran Associates, Inc. URL: https://proceedings.neurips.cc/paper/2017/file/892c3b1c6dccd52936e27cbd0ff683d6-Paper.pdf

Hartley, R., Zisserman, A., 2003. Multiple View Geometry in Computer Vision. Cambridge University Press, New York, USA. https://doi.org/10.1017/CBO9780511811685

Karras, T., Laine, S., Aila, T., 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4396-4405. https://doi.org/10.1109/CVPR.2019.00453

Kendall, A., Cipolla, R., 2017. Geometric Loss Functions for Camera Pose Regression with Deep Learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6555-6564. https://doi.org/10.1109/CVPR.2017.694

Kendall, A., Grimes, M., Cipolla, R., 2015. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization. In: Proceedings of the International Conference on Computer Vision. pp. 2938-2946. https://doi.org/10.1109/ICCV.2015.336

Krizhevsky, A., Sutskever, I., Hinton, G. E., 2012. ImageNet Classification with Deep Convolutional Neural Networks. In: Pereira, F., Burges, C.J. C., Bottou, L., Weinberger, K. Q. (Eds.), Proceedings of the Advances in Neural Information Processing Systems. Vol. 25. Curran Associates, Inc.URL:https://proceedings.neurips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf

Krombach, N., Droeschel, D., Behnke, S., 2016. Combining Feature-based and Direct Methods for Semi-dense Real-time Stereo Visual Odometry. In: Proceedings of the International Conference on Intelligent Autonomous Systems. pp. 855-868. https://doi.org/10.1007/978-3-319-48036-7_62

LeCun, Y., Bengio, Y., Hinton, G., 2015. Deep learning. Nature 521 (7553),436. URL: https://www.nature.com/articles/nature14539 https://doi.org/10.1038/nature14539

Li, R., Wang, S., Long, Z., Gu, D., 2018. UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning. In: Proceedings of the IEEE International Conference on Robotics and Automation. pp. 7286-7291. https://doi.org/10.1109/ICRA.2018.8461251

Li, S., Xue, F., Wang, X., Yan, Z., Zha, H., 2019. Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry. In: Proceedings of the International Conference on Computer Vision. https://doi.org/10.1109/ICCV.2019.00294

Lowe, D. G., 1999. Object recognition from local scale-invariant features. In:Proceedings of the International Conference on Computer Vision. pp. 1150-1157. https://doi.org/10.1109/ICCV.1999.790410

Min, Z., Yang, Y., Dunn, E., 2020. VOLDOR: Visual Odometry From LogLogistic Dense Optical Flow Residuals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4897-4908. https://doi.org/10.1109/CVPR42600.2020.00495

Mur-Artal, R., Montiel, J. M. M., Tardós, J. D., 2015. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics,1147-1163. https://doi.org/10.1109/TRO.2015.2463671

Mur-Artal, R., Tardós, J. D., 2017. ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. IEEE Transactions on Robotics, 1255-1262. https://doi.org/10.1109/TRO.2017.2705103

Nistér, D., Naroditsky, O., Bergen, J., 2004. Visual odometry. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp.652-659. https://doi.org/10.1109/CVPR.2004.1315094

Pire, T., Fischer, T., Castro, G., De Cristóforis, P., Civera, J., Jacobo Berlles, J.,2017. S-PTAM: Stereo Parallel Tracking and Mapping. Journal of Robotics and Autonomous Systems, 27-42. https://doi.org/10.1016/j.robot.2017.03.019

Radford, A., Metz, L., Chintala, S., 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. In: Computing Research Repository (CoRR).URL:http://arxiv.org/abs/1511.06434

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.,2016. Improved Techniques for Training GANs. In: Proceedings of the International Conference on Neural Information Processing Systems. pp. 2234-2242.

Scaramuzza, D., Fraundorfer, F., 2011. Visual Odometry [Tutorial]. IEEE Robotics and Automation Magazine, 80-92. https://doi.org/10.1109/MRA.2011.943233

Siciliano, B., Khatib, O., 2016. Springer Handbook of Robotics. Springer Publishing Company, Incorporated. https://doi.org/10.1007/978-3-319-32552-1

Tateno, K., Tombari, F., Laina, I., Navab, N., 2017. CNN-SLAM: Real-Time Dense Monocular SLAM with Learned Depth Prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp.6565-6574. https://doi.org/10.1109/CVPR.2017.695

Thrun, S., Burgard, W., Fox, D., 2005. Probabilistic Robotics. The MIT Press.

Tulyakov, S., Liu, M.-Y., Yang, X., Kautz, J., 2018. MoCoGAN: Decomposing motion and content for video generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1526-1535. https://doi.org/10.1109/CVPR.2018.00165

Umeyama, S., 1991. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 376-380. https://doi.org/10.1109/34.88573

Wang, S., Clark, R., Wen, H., Trigoni, N., 2017. DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks. In:Proceedings of the IEEE International Conference on Robotics and Automation. pp. 2043-2050. https://doi.org/10.1109/ICRA.2017.7989236

Yang, N., Wang, R., Stückler, J., Cremers, D., 2018. Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry. In: Proceedings of the European Conference on Computer Vision. pp. 835-852. https://doi.org/10.1007/978-3-030-01237-3_50

Yi, X., Walia, E., Babyn, P., 2019. Generative adversarial network in medical imaging: A review. Medical Image Analysis 58, 101552.URL:https://www.sciencedirect.com/science/article/pii/S1361841518308430 https://doi.org/10.1016/j.media.2019.101552

Yin, Z., Shi, J., 2018. GeoNet: Unsupervised Learning of Dense Depth, OpticalFlow and Camera Pose. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1983-1992. https://doi.org/10.1109/CVPR.2018.00212

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem