Обзор современных методов визуальной одометрии

Михаил Андреевич Терехов

doi:10.32603/2071-2340-2019-3-5-14

Михаил Андреевич Терехов Санкт-Петербургский государственный университет, Университетский проспект, д. 28, 198504, Старый Петергоф, Санкт-Петербург, Россия

DOI: https://doi.org/10.32603/2071-2340-2019-3-5-14

Ключевые слова: визуальная одометрия, SLAM, автономная навигация, ADAS, UAV

Аннотация

В этой статье описываются задачи систем визуальной одометрии и SLAM и их основные применения. Далее перечисляются основные подходы, использованные научным сообществом для создания таких систем в разное время. Мы также углубляемся в более современный метод, основанный на совместной оптимизации, и разбираем его вариации в зависимости от требований к решению. Наконец, мы рассматриваем современные направления исследований в области визуальной одометрии и кратко представляем свои наработки.

Биография автора

Михаил Андреевич Терехов, Санкт-Петербургский государственный университет, Университетский проспект, д. 28, 198504, Старый Петергоф, Санкт-Петербург, Россия

Студент четвёртого курса математико-механического факультета СПбГУ, st054464@student.spbu.ru

Литература

Google cartographer. [Online]. Available: https://github.com/googlecartographer/cartographer

“Multicamera dso,” GitHub. [Online]. Available: https://github.com/MikhailTerekhov/mdso

Y. Almalioglu, M. R. U. Saputra, P. P. B. de Gusmão, A. Markham, and N. Trigoni, “GANVO: Unsupervised Deep Monocular Visual Odometry and Depth Estimation with Generative Adversarial Networks,” in 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 5474–5480; doi: 10.1109/ICRA.2019.8793512

J. Engel, V. Koltun, and D. Cremers, “Direct sparse odometry,” IEEE transactions on pattern analysis and machine intelligence, vol. 40, no. 3, pp. 611–625, 2018; doi: 10.1109/TPAMI.2017.2658577

J. Engel, T. Schöps, and D. Cremers, “Lsd-slam: Large-scale direct monocular slam,” in Computer Vision – ECCV 2014, Zurich, Switzerland, pp. 834–849, 2014; doi: 10.1007/978-3-319-10605-2_54

J. Engel, T. Schöps, and D. Cremers, “Large-scale direct SLAM with stereo cameras,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany, 2015, pp. 1935–1942; doi: 10.1109/IROS.2015.7353631

J.-M. Frahm et al., “Building Rome on a cloudless day,” in Computer Vision – ECCV 2010, Heraklion, Crete, Greece, 2010, pp. 368–381; doi: 10.1007/978-3-642-15561-1_27

R. Hartley and A. Zisserman, Multiple view geometry in computer vision, Cambridge, UK: Cambridge university press, 2003.

J. Heinly, J. L. Schonberger, E. Dunn, and J.-M. Frahm, “Reconstructing the world* in six days *(as captured by the yahoo 100 million image dataset),” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015, pp. 3287–3295.

G. Klein and D. Murray, “Parallel tracking and mapping for small ar workspaces,” in Proc. of the 2007 6th IEEE and ACM Int. Symp. on Mixed and Augmented Reality, Washington, DC, 2007, pp. 1–10; doi: 10.1109/ISMAR.2007.4538852

T. Lacey, “Tutorial: The kalman filter,” MIT. [Online]. Available: http://web.mit.edu/kirtley/kirtley/binlustuff/literature/control/Kalman%20filter.pdf

H. Matsuki, L. von Stumberg, V. Usenko, J. Stuckler, and D. Cremers, ¨ “Omnidirectional DSO: Direct Sparse Odometry With Fisheye Cameras,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 3693–3700, 2018; doi: 10.1109/LRA.2018.2855443

A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint kalman filter for vision-aided inertial navigation,” in Proc. 2007 IEEE Int. Conf. on Robotics and Automation, Roma, Italy, 2007, pp. 3565–3572; doi: 10.1109/ROBOT.2007.364024

R. Mur-Artal, J. M. M. Montiel, and J. D. Tard´os, “Orb-slam: A versatile and accurate monocular slam system,” Transactions on Robotics, vol. 31, no. 5, pp. 1147–1163, 2015; doi: 10.1109/TRO.2015.2463671

R. Mur-Artal and J. D. Tardós, “ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras,” Transactions on Robotics, vol. 33, no. 5, pp. 1255–1262, 2017; doi: 10.1109/TRO.2017.2705103

R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “DTAM: Dense tracking and mapping in realtime,” in 2011 International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 2320–2327; doi: 10.1109/ICCV.2011.6126513

E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” in European Conf. on Computer Vision, 2011, pp. 2564–2571.

H. Strasdata, J. M. M. Montiel, and A. J. Davisona, “Visual slam: why filter?” Image and Vision Computing, vol. 30, no. 2, pp. 65–77, 2012; doi: 10.1016/j.imavis.2012.02.009

K. Tateno, F. Tombari, I. Laina, and N. Navab, “Cnn-slam: Real-time dense monocular slam with learned depth prediction,” in Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, Hawaii, USA, 2017, pp. 6243–6252.

M. J. Tribou, A. Harmat, D. W. L. Wang, I. Sharf, and S. L. Waslander, “Multi-camera parallel tracking and mapping with non-overlapping fields of view,” The International Journal of Robotics Research, vol. 34, no. 12, pp. 1480–1500, 2015; doi: 10.1177/0278364915571429

S. Urban and S. Hinz, “Multicol-slam-a modular real-time multi-camera slam system,” in arXiv preprint, arXiv:1610.07336, 2016. [Online]. Available: https://arxiv.org/abs/1610.07336

R. Wang, M. Schworer, and D. Cremers, “Stereo dso: Large-scale direct sparse visual odometry with stereo cameras,” in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 3903–3911.

N. Yang, R. Wang, J. Stuckler, and D. Cremers, “Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 817–833.