Visual-Based Simultaneous Localization and Mapping (VSLAM) Techniques for Robots: A Scientific Review
DOI:
https://doi.org/10.25007/ajnu.v12n3a1500Abstract
The main problem facing autonomous robots is to navigate in an environment with the ability to determine its location and simultaneously build a map, SLAM technique can formulate this requirement efficiently. In this paper Filter-based, Graph-based, and AI-based Visual-SLAM techniques have been reviewed. The review shows that the first method suffers from high computations when the number of landmarks increases. The Graph-based algorithms are exposed to drift-error problems which cause a delocalization and require optimization. The AI-based vSLAM has the advantage of not-having complicated mathematical models in the algorithm, and it shows an efficient performance in various environments. The reviewed algorithms utilize different cameras including mono, stereo, and RGB-D cameras. The low-cost RGB-D cameras encourage implementation in modern autonomous robots. This work introduces a scientific-based overview of vSLAM to the reader, by explaining all phases of SLAM, the state-of-the-art algorithms, highlighting the strengths and weaknesses of each paradigm.
Downloads
References
Ai, Y.-b., Rui, T., Yang, X.-q., He, J.-l., Fu, L., Li, J.-b., & Lu, M. (2020). Visual SLAM in dynamic environments based on object detection. Defence Technology(2214-9147), 1-10. doi:10.1016/j.dt.2020.09.012
Alismail, H., Browning, B., & Lucey, S. (2016). Photometric Bundle Adjustment for Vision-Based SLAM. In Computer Vision – ACCV 2016 (pp. 324–341). Cham: Springer International Publishing. doi:10.1007/978-3-319-54190-7 20
Alsadik, B., & Karam, S. (2021). The Simultaneous Localization and Mapping (SLAM)-An Overview. SURVEYING AND GEOSPATIAL ENGINEERING JOURNAL, 2(1), 1-12.
Artal, M. R., & Tardos, J. D. (2017). ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras. IEEE Transactions on Robotics, 33(5), 1255-1262. doi:10.1109/TRO.2017.2705103
Artal, R. M., Montiel, J. M., & Tardos, J. D. (2015). ORB-SLAM: a Versatile and Accurate Monocular SLAM System. IEEE Transactions on Robotics, 31(5), 1147-1163. doi:10.1109/TRO.2015.2463671
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 50(2), 174-188.
Benavidez, P., Kumar, M., & Jamshidi, M. (2014). Improving Visual SLAM Algorithms for use in Realtime Robotic Applications. 2014 World Automation Congress (WAC) (pp. 1-6). Waikoloa: IEEE. doi:10.1109/WAC.2014.7084333
Bensalem, S., Gallien, M., Ingrand, F., Kahloul, I., & Nguyen, T.-H. (2008). Toward a More Dependable Software Architecture for Autonomous Robots. IEEE ROBOTICS AND AUTOMATION MAGAZINE.
Birk, A., & Pfingsthorn, M. (2016). Simultaneous Localization And Mapping (SLAM). Encyclopedia of Electrical and Electronics Engineering: Wiley, 1-41.
Bruno, H. M., & Colombini, E. L. (2021). LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method. Neurocomputing, 455, 97-110. doi:10.1016/j.neucom.2021.05.027
Campos, C., Elvira, R., Rodrıguez, J. G., Montiel, J. M., & Tardos, J. D. (2021). ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM. IEEE Transactions on Robotics, 1-17. doi:10.1109/TRO.2021.3075644
Chen, L., Jin, S., & Xia, Z. (2021). Towards a Robust Visual Place Recognition in Large-Scale vSLAM Scenarios Based on a Deep Distance Learning. Sensors, 21(1), 1-19. doi:10.3390/s21010310
Correll, N. (2016). Introduction to Autonomous Robots. Colorado: Wiely.
Davis, T. A. (2006). Direct Methods for Sparse Linear Systems. SIAM,. In SIAM Book Series on the Fundamentals of Algorithms. Florida.
Davison, A. J. (2003). Real-Time Simultaneous Localisation and Mapping with a Single Camera. Ninth IEEE International Conference on Computer Vision, (pp. 1403–1410).
Dellaert, F., & Kaess, M. (2006). Square root SAM: Simultaneous location and mapping via square root information smoothing. Journal of Robotics, 1, 1-32.
Dib, A., Beaufort, N., & Charpillet, F. (2014). A Real Time Visual SLAM For RGB-D Cameras Based on Chamfer Distance and Occupancy Grid. IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Besançon.
Duan, C., Junginger, S., Huang, J., Jin, K., & Thurow, K. (2019). Deep Learning for Visual SLAM in Transportation Robots: A review. Transportation Safety and Environment, 1(3), 177–184. doi:10.1093/tse/tdz019
Durrant-Whyte, H., Rye, D., & Nebot, E. (1995). Localization of Autonomous Guided Vehicles. Munich, German.
Grisetti, G., K'ummerle, R., Stachniss, C., & Burgard, W. (2011). A Tutorial on Graph-Based SLAM. IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZIN, 1, 31-43. doi:10.1109/MITS.2010.939925
Inc, M. (n.d.). Monocular Visual Simultaneous Localization and Mapping. (MathWorks) Retrieved 8 18, 2021, from https://www.mathworks.com/help/vision/ug/monocular-visual-simultaneous-localization-and-mapping.html
Jajulwar, K. K., & Deshmukh, A. Y. (2013). Design of Mobile Robot Navigation System using vSLAM and Distributed Filter Techniques. IEEE Sixth International Conference on Emerging Trends in Engineering and Technolog. Nagpur. doi:10.1109/ICETET.2013.35
Jiménez, J. E., Devy, M., & Gordillo, J. (2016). Visual EKF-SLAM from Heterogeneous Landmarks. Sensors,, 16(4), 1-26. doi:10.3390
Julier, S. J., & Uhlmann, J. K. (2001). A counter example to the theory of simultaneous localization and map building. IEEE International Conference on Robotics 8 Automation. Seoul.
Karam, S., Lehtola, V., & Vosselman, G. (2019). INTEGRATING A LOW COST MEMS IMU INTO A LASER BASED SLAM for INDOOR MOBILE MAPPING. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2, 149-156.
Khairuddin, A. R., Talib, M. S., & Haron, H. (2015). Review on Simultaneous Localization and Mapping (SLAM). IEEE International Conference on Control System, Computing and Engineering,, (pp. 85-90). Penang, Malaysia.
Leonard, J. J., & Durrant-Whyte, H. F. (1991). Mobile Robot Localization by Tracking Geometric. IEEE TRANSACTIONS ON ROBOTICS AND AUTOMATION, 7(3), 376-382.
Li, G., Yu, L., & Fei, S. (2021). A deep-learning real-time visual SLAM system based on multi-task feature extraction network and self-supervised feature points. Measurement, 168, 1-10. doi:10.1016/j.measurement.2020.108403
Liu, Y., & Miura, J. (2021). KMOP-vSLAM Dynamic Visual SLAM for RGB-D Cameras using K-means and OpenPose. IEEE/SICE International Symposium on System Integration, (pp. 415 - 420). Fukushima. doi:10.1109/IEEECONF49454.2021.9382724
LIU, Y., & MIURA, J. (2021). RDMO-SLAM Real-Time Visual SLAM for Dynamic Environments Using Semantic Label Prediction With Optical Flow. IEEE Access, 9, 106981 - 106997.
Lu, F., & MILIOS, E. (1997). Globally Consistent Range Scan Alignment for Environment Mapping. Autonomous Robots , 4, 333–349.
Makhubela, J. K., Zuva, T., & Agunbiade, O. Y. (2019). Vision based Simultaneous Localization and Mapping in a light intensity Static Environment. International Journal of Scientific Research & Engineering Technology (IJSET), 13, 22-27.
Maxime, F., Alexandre, E., Juli, M., Martial, S., & Guy, L. B. (2021). OV2SLAM: A Fully Online and Versatile Visual SLAM forvReal-Time Applications. IEEE Robotics and Automation Letters, 6(2), 1399-1406.
Maybeck, S. P. (1979). Stochastic Models, Estimaton and Control, (Vol. 1). Ohio: Academic Press,.
MOHAMED, S. A., HAGHBAYAN, M. H., WESTERLUND, T., HEIKKONEN, J., TENHUNEN, H., & PLOSILA, J. (2019). A Survey on Odometry for Autonomous Navigation Systems. IEEE Access, 97466-97486.
Muhammed, N., Fosi, D., & Ainouz, S. (2009). Current state of the art of vision based SLAM. The International Society for Optical Engineering, (pp. 1-13).
Mur-Artal, R., Montiel, J., & Tard´os, J. D. (2015). ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE TRANSACTIONS ON ROBOTICS, 1-17. doi:10.1109/TRO.2015.2463671
N. G. Hockstein, C. G. Gourin, R. A. Faust, & D. J. Terris. (2007, March 17). A history of robots: from science Wction to surgical robotics. Springer, 1, 113–118. doi:DOI 10.1007/s11701-007-0021-2
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011). DTAM: Dense Tracking and Mapping in Real-Time. IEEE International Conference on Computer Vision, (pp. 2320–2327).
Olson, E., Leonard, J., & Teller, S. (2006). Fast iterative optimization of pose graphs with poor initial estimates. The IEEE Int. Conf. on Robotics & Automation (ICRA), 2262–2269.
Pumarola, A., Vakhitov, A., & Agudo, A. (2017). PL-SLAM: Real-Time Monocular Visual SLAM with Points and Lines. IEEE International Conference on Robotics and Automation (ICRA). Singapore. doi:10.1109/ICRA.2017.7989522
Saeedi, S., Trentini, M., Seto, M., & Li, H. (2016). Multiple-Robot Simultaneous Localization and Mapping: A Review. Journal of Field Robotics, 33(1), 3-46. doi:0.1002/rob.21620
Siegwart, R., & Nourbakhsh, I. R. (2004). Intoduction To Autonomous Mobile Robot. London: The MIT Press.
Smith, R. C., & Cheeseman, P. (1986). On the Representation and Estimation of Spatial Uncertainty. The International Journal of Robotics Research, 5(4), 56-68.
Sorenson, H. W. (1966). Kalman Filtering Techniques. Advances in Control Systems, 3, 219-292.
Taketomi, T., Uchiyama, H., & Ikeda, S. (2017). Visual SLAM algorithms: a survey from 2010 to 2016. Transactions on Computer Vision and Applications, 1, 1-11. doi:10.1186/s41074-017-0027-2
Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic Robotics (Intelligent Robotics and Autonomous Agents). The MIT Press.
Vincent, J., Labbe, M., Lauz, J. S., Grondin, F., Comtois-Rivet, P.-M., & Michaud, F. (2020). Dynamic Object Tracking and Masking for Visual SLAM. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4974-4979). Las Vegas: IEEE.
Wallén, J. (2008). The history of the industrial robot. Linköpings universitet, Electrical Engineering. Linköping, Sweden: Linköpings universitet poblications.
Wang, X. (2018). Autonomous Mobile Robot Visual SLAM Based on Improved CNN Method. IOP Conference Series: Materials Science and Engineering, 466, pp. 1-8. Nanjing. doi:10.1088/1757-899X/466/1/012114
Whyte, H. D., & Bailey, T. (2006). Simultaneous Localisation and Mapping (SLAM) Part I The Essential Algorithms. IEEE Robotics & Automation Magazine, 1, 99-108.
Zhang, S., Lu, S., He, R., & Bao, Z. (2021). Stereo Visual Odometry Pose Correction through Unsupervised Deep Learning. Sensors, 21, 4735 - 4,753. doi:10.3390/s21144735
Zhou, F., Zhang, L., Deng, C., & Fan, X. (2021). Improved Point-Line Feature Based Visual SLAM Method for Complex Environments. sensors, 21(13), 1-17. doi:10.3390/s21134604
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Academic Journal of Nawroz University
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors retain copyright
The use of a Creative Commons License enables authors/editors to retain copyright to their work. Publications can be reused and redistributed as long as the original author is correctly attributed.
- Copyright
- The researcher(s), whether a single or joint research paper, must sell and transfer to the publisher (the Academic Journal of Nawroz University) through all the duration of the publication which starts from the date of entering this Agreement into force, the exclusive rights of the research paper/article. These rights include the translation, reuse of papers/articles, transmit or distribute, or use the material or parts(s) contained therein to be published in scientific, academic, technical, professional journals or any other periodicals including any other works derived from them, all over the world, in English and Arabic, whether in print or in electronic edition of such journals and periodicals in all types of media or formats now or that may exist in the future. Rights also include giving license (or granting permission) to a third party to use the materials and any other works derived from them and publish them in such journals and periodicals all over the world. Transfer right under this Agreement includes the right to modify such materials to be used with computer systems and software, or to reproduce or publish it in e-formats and also to incorporate them into retrieval systems.
- Reproduction, reference, transmission, distribution or any other use of the content, or any parts of the subjects included in that content in any manner permitted by this Agreement, must be accompanied by mentioning the source which is (the Academic Journal of Nawroz University) and the publisher in addition to the title of the article, the name of the author (or co-authors), journal’s name, volume or issue, publisher's copyright, and publication year.
- The Academic Journal of Nawroz University reserves all rights to publish research papers/articles issued under a “Creative Commons License (CC BY-NC-ND 4.0) which permits unrestricted use, distribution, and reproduction of the paper/article by any means, provided that the original work is correctly cited.
- Reservation of Rights
The researcher(s) preserves all intellectual property rights (except for the one transferred to the publisher under this Agreement).
- Researcher’s guarantee
The researcher(s) hereby guarantees that the content of the paper/article is original. It has been submitted only to the Academic Journal of Nawroz University and has not been previously published by any other party.
In the event that the paper/article is written jointly with other researchers, the researcher guarantees that he/she has informed the other co-authors about the terms of this agreement, as well as obtaining their signature or written permission to sign on their behalf.
The author further guarantees:
- The research paper/article does not contain any defamatory statements or illegal comments.
- The research paper/article does not violate other's rights (including but not limited to copyright, patent, and trademark rights).
This research paper/article does not contain any facts or instructions that could cause damages or harm to others, and publishing it does not lead to disclosure of any confidential information.
