OG-SLAM: A real-time and high-accurate monocular visual SLAM framework
Article Sidebar
Main Article Content
Abstract
The challenge of improving the accuracy of monocular Simultaneous Localization and Mapping (SLAM) is considered, which widely appears in computer vision, autonomous robotics, and remote sensing. A new framework (ORB-GMS-SLAM (or OG-SLAM)) is proposed, which introduces the region-based motion smoothness into a typical Visual SLAM (V-SLAM) system. The region-based motion smoothness is implemented by integrating the Oriented Fast and Rotated Brief (ORB) features and the Grid-based Motion Statistics (GMS) algorithm into the feature matching process. The OG-SLAM significantly reduces the absolute trajectory error (ATE) on the key-frame trajectory estimation without compromising the real-time performance. This study compares the proposed OG-SLAM to an advanced V-SLAM system (ORB-SLAM2). The results indicate the highest accuracy improvement of almost 75% on a typical RGB-D SLAM benchmark. Compared with other ORB-SLAM2 settings (1800 key points), the OG-SLAM improves the accuracy by around 20% without losing performance in real-time. The OG-SLAM framework has a significant advantage over the ORB-SLAM2 system in that it is more robust for rotation, loop-free, and long ground-truth length scenarios. Furthermore, as far as the authors are aware, this framework is the first attempt to integrate the GMS algorithm into the V-SLAM.
Downloads
Article Details
Copyright (c) 2022 Kuang B.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Licensing and protecting the author rights is the central aim and core of the publishing business. Peertechz dedicates itself in making it easier for people to share and build upon the work of others while maintaining consistency with the rules of copyright. Peertechz licensing terms are formulated to facilitate reuse of the manuscripts published in journals to take maximum advantage of Open Access publication and for the purpose of disseminating knowledge.
We support 'libre' open access, which defines Open Access in true terms as free of charge online access along with usage rights. The usage rights are granted through the use of specific Creative Commons license.
Peertechz accomplice with- [CC BY 4.0]
Explanation
'CC' stands for Creative Commons license. 'BY' symbolizes that users have provided attribution to the creator that the published manuscripts can be used or shared. This license allows for redistribution, commercial and non-commercial, as long as it is passed along unchanged and in whole, with credit to the author.
Please take in notification that Creative Commons user licenses are non-revocable. We recommend authors to check if their funding body requires a specific license.
With this license, the authors are allowed that after publishing with Peertechz, they can share their research by posting a free draft copy of their article to any repository or website.
'CC BY' license observance:
|
License Name |
Permission to read and download |
Permission to display in a repository |
Permission to translate |
Commercial uses of manuscript |
|
CC BY 4.0 |
Yes |
Yes |
Yes |
Yes |
The authors please note that Creative Commons license is focused on making creative works available for discovery and reuse. Creative Commons licenses provide an alternative to standard copyrights, allowing authors to specify ways that their works can be used without having to grant permission for each individual request. Others who want to reserve all of their rights under copyright law should not use CC licenses.
Saputra MRU, Markham A, Trigoni N. Visual SLAM and Structure from Motion in Dynamic Environments. ACM Comput Surv. 2018; 51:1–36.
Geromichalos D, Azkarate M, Tsardoulias E, Gerdes L, Petrou L, Perez Del Pulgar C. SLAM for autonomous planetary rovers with global localization. J F Robot. 2020; 37: 830–847.
Li G, Geng Y, Zhang W. Autonomous planetary rover navigation via active SLAM. Aircr Eng Aerosp Technol. 91: 60–68.
Francis SLX, Anavatti SG, Garratt M, Shim H. A ToF-Camera as a 3D Vision Sensor for Autonomous Mobile Robotics. Int J Adv Robot Syst. 12: 156.
Younes G, Asmar D, Shammas E, Zelek J. Keyframe-based monocular SLAM: design, survey, and future directions. Rob Auton Syst. 98: 67-88.
Yu F, Shang J, Hu Y, Milford M. NeuroSLAM: a brain-inspired SLAM system for 3D environments. Biol Cybern. 113: 5-6. 515-545,
Nüchter A, Lingemann K, Hertzberg J, Surmann H. 6D SLAM - 3D mapping outdoor environments. J F Robot. 2007; 24: 699-722.
Kuang B, Rana Z, Zhao Y. A Novel Aircraft Wing Inspection Framework based on Multiple View Geometry and Convolutional Neural Network. Aerosp Eur Conf 2020.
Hewitt RA. The Katwijk beach planetary rover dataset. Int J Rob Res. 37: 3-12, 2018.
Furgale P, Carle P, Enright J, Barfoot TD. The Devon Island rover navigation dataset. Int J Rob Res. 2012; 31: 707–713.
Kuang B, Rana ZA, Zhao Y. Sky and Ground Segmentation in the Navigation Visions of the Planetary Rovers. Sensors (Basel). 2021 Oct 21;21(21):6996. doi: 10.3390/s21216996. PMID: 34770302; PMCID: PMC8588092.
Compagnin A. Autoport project: A docking station for planetary exploration drones. AIAA SciTech Forum - 55th AIAA Aerosp Sci Meet. 2017.
Dubois R, Eudes A, Fremont V. AirMuseum: a heterogeneous multi-robot dataset for stereo-visual and inertial Simultaneous Localization and Mapping. IEEE Int Conf Multisens Fusion Integr Intell Syst 2020; 2020: 166-172.
Chiodini S, Torresin L, Pertile M, Debei S. Evaluation of 3D CNN Semantic Mapping for Rover Navigation. 2020 IEEE 7th International Workshop on Metrology for AeroSpace (MetroAeroSpace). 2020; 32–36.
Opower H. Multiple view geometry in computer vision. Opt. Lasers Eng. 37;1: 2002; 85–86.
Williams B, Cummins M, Neira J, Newman P, Reid I,Tardós J. A comparison of loop closing techniques in monocular SLAM. Rob Auton Syst 57; 12: 2009; 1188–1197.
Polvi J, Taketomi T, Yamamoto G, Dey A, Sandor C, Kato H. SlidAR: A 3D positioning method for SLAM-based handheld augmented reality Comput Graph 2016; 55: 33-43.
Chen SY. Kalman Filter for Robot Vision: A Survey. IEEE Trans Ind Electron 59: 4409-4420.
Castellanos JA, Neira J, Tardós JD. Limits to the consistency of EKF-based SLAM. IFAC Proc 37; 8: 2004; 716–721.
Triggs B, McLauchlan PF, R I. Hartley, Fitzgibbon AW Bundle Adjustment — A Modern Synthesis. 2000; 298–372.
Strasdat H, Montiel JMM, Davison AJ. Real-time monocular SLAM: Why filter? In 2010 IEEE International Conference on Robotics and Automation 2010; 2657–2664.
Cui H, Shen S, Gao W, Wang Z. Progressive Large-Scale Structure-from-Motion with Orthogonal MSTs. In 2018 International Conference on 3D Vision (3DV) 2018; 79–88.
Wang T, Lv G, Wang S, Li H, Lu B. SIFT Based Monocular SLAM with GPU Accelerated. In Lecture Notes of the Institute for Computer Sciences. Social-Informatics and Telecommunications Engineering, LNICST 237 LNICST 2018; 13–22.
Bian J, Lin W. GMS : Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence 4181–4190.
Nie S, Jiang Z, Zhang H, Wei Q, Image matching for space objects based on grid-based motion statistics. 875 Springer Singapore 2018.
Zhang X, Xie Z. Reconstructing 3D Scenes from UAV Images Using a Structure-from-Motion Pipeline. In 2018 26th International Conference on Geoinformatics. 2018; 2018:1–6.
Yan K, Han M. Aerial Image Stitching Algorithm Based on Improved GMS. In 2018 Eighth International Conference on Information Science and Technology (ICIST) 2018; 351–357.
Nobre F, Kasper M, Heckman C. Drift-correcting self-calibration for visual-inertial SLAM. In 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017; 6525–6532.
Mur-Artal R, Tardos JD. ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras. IEEE Trans Robot 33; 5: 2017; 1255–1262.
Shiozaki T, Dissanayake G. Eliminating Scale Drift in Monocular SLAM Using Depth From Defocus. IEEE Robot. Autom. Lett 3; 1:2018; 581–587.
Muja M, Lowe DG. Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP 2009 - Proc. 4th Int. Conf. Comput. Vis. Theory Appl., 1:2009; 331–340.
Rublee E, Rabaud V, Konolige K, Bradski G. ORB: An efficient alternative to SIFT or SURF. In 2011 International Conference on Computer Vision. 2011; 2564–2571.
Rosten E, Drummond T. Machine Learning for High-Speed Corner Detection. 2006; 430–443.
Harris C, Stephens M. A Combined Corner and Edge Detector. in Procedings of the Alvey Vision Conference. 1988;1988: 69; 23.1-23.6.
Kirkwood JR, Kirkwood BH. Elementary Linear Algebra. Chapman and Hall/CRC, 2017.
Ourselin S, Roche A, Subsol G, Pennec X, Ayache N. Reconstructing a 3D structure from serial histological sections. Image Vis Comput 19; 1–2: 2001; 25–31.
Beevers KR, Huang WH. SLAM with sparse sensing. In Proceedings 2006 IEEE International Conference on Robotics and Automation. 2006. ICRA 2006; 2006: 2006; 2285–2290.
Sturm J, Engelhard N, Endres F, Burgard W, Cremers D. A benchmark for the evaluation of RGB-D SLAM systems. In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems. 2012; 573–580.
Mur-Artal R, Montiel JM M, Tardos JD. ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Trans. Robot 31; 5: 2015; 1147–1163.
Fossum ER. CMOS image sensors: electronic camera-on-a-chip. IEEE Trans Electron Devices 44;10: 1997; 1689–1698.