SEMANTIC MAPPING BASED ON VISUAL SLAM AND YOLOV5

Dawei Wang, Yuanwei Bi, Guohui Wang, and Yaowen Liu

References

  1. [1] I.A. Kazerouni, L. Fitzgerald, G. Dooly, and D. Toal, Asurvey of state-of-the-art on visual SLAM, Expert Systems withApplications, 205, 2022, 117734.
  2. [2] C. Chen, B. Wang, C.X. Lu, A. Trigoni, and A.Markham, A survey on deep learning for localization andmapping: Towards the age of spatial machine intelligence,ArXiv, abs/2006.12567, 2020. Available: https://api.semanticscholar.org/CorpusID:219981387
  3. [3] A. Tourani, H. Bavle, J.L. Sanchez-Lopez, and H. Voos,Visual SLAM: What are the current trends and what toexpect? Sensors, 22(23), 2022, 9297. Available: https://api.semanticscholar.org/CorpusID:252993134
  4. [4] R. Mur-Artal, J.M.M. Montiel, and J.D. Tardo’s, ORB-SLAM:A versatile and accurate monocular SLAM system, IEEETransactions on Robotics, 31(5), 2015, 1147–1163.
  5. [5] R. Mur-Artal and J.D. Tardo’s, ORB-SLAM2: An open-sourceSLAM system for monocular, stereo, and RGB-D cameras,IEEE Transactions on Robotics, 33(5), 2017, 1255–1262.
  6. [6] C. Campos, R. Elvira, J.J.G. Rodr’ıguez, J.M.M. Montiel,and J.D. Tardo’s, ORB-SLAM3: An accurate open-sourcelibrary for visual, visual–inertial, and multimap SLAM, IEEETransactions on Robotics, 37(6), 2021, 1874–1890.
  7. [7] L.-F. Shi, F. Zheng, and Y. Shi, Multi-constraint slamoptimisation algorithm for indoor scenes, International Journalof Robotics & Automation, 38(5), 2023, 375–382.
  8. [8] X. Zhang, W. Wang, X. Qi, Z. Liao, and R. Wei, Point-planeSLAM using supposed planes for indoor environments, Sensors,19(17), 2019, 3795. Available: https://www.mdpi.com/1424-8220/19/17/3795
  9. [9] Y. Li, R. Yunus, N. Brasch, N. Navab, and F. Tombari,RGB-D SLAM with structural regularities, in Proceeding IEEEInternational Conf. on Robotics and Automation (ICRA),Xi’an, China, 2021, 11581–11587.
  10. [10] R. Yunus, Y. Li, and F. Tombari, ManhattanSLAM: Robustplanar tracking and mapping leveraging mixture of Manhattanframes, in Proceeding IEEE International Conf on Roboticsand Automation (ICRA), Xi’an, China, 2021, 6687–6693.
  11. [11] J. Straub, G. Rosman, O. Freifeld, J.J. Leonard, and J.W.Fisher, A mixture of manhattan frames: Beyond the manhattanworld, in Proceeding IEEE Conf. on Computer Vision andPattern Recognition, Columbus, OH, USA, 2014, 3770–3777.
  12. [12] K. Wang, F. Gao, and S. Shen, Real-time scalable densesurfel mapping, in Procedding International Conf. on Roboticsand Automation (ICRA), Montr´eal, QC, Canada, 2019,6919–6925.
  13. [13] F. Shu, J. Wang, A. Pagani, and D. Stricker, Structure PLP-SLAM: Efficient sparse mapping and localization using point,line and plane for monocular, RGB-D and stereo cameras,in Proceeding IEEE International Conf. on Robotics andAutomation (ICRA), London, U.K., 2023, 2105–2112.
  14. [14] K. Chen, J. Zhang, J. Liu, Q. Tong, R. Liu, andS. Chen, Semantic visual simultaneous localization andmapping: A survey, ArXiv, abs/2209.06428, 2022. Available:https://api.semanticscholar.org/CorpusID:252221498
  15. [15] N. Su¨nderhauf, T. T. Pham, Y. Latif, M. Milford, and I.Reid, Meaningful maps with object-oriented semantic mapping,in Proceeding IEEE/RSJ International Conf. on IntelligentRobots and Systems (IROS), Vancouver, BC, Canada, 2017,5079–5085.
  16. [16] X. Hu, Y. Zhang, Z. Cao, R. Ma, Y. Wu, Z. Deng, and W. Sun,CFP-SLAM: A real-time visual slam based on coarse-to-fineprobability in dynamic environments, in Proceeding IEEE/RSJInternational Conf. on Intelligent Robots and Systems (IROS),Kyoto, Japan, 2022, 4399–4406.
  17. [17] C. Yu, Z. Liu, X.-J. Liu, F. Xie, Y. Yang, Q. Wei, andQ. Fei, DS-SLAM: A semantic visual slam towards dynamicenvironments, in Proceeding IEEE/RSJ International Conf. onIntelligent Robots and Systems (IROS), Madrid, Spain, 2018,1168–1174.
  18. [18] V. Badrinarayanan, A. Kendall, and R. Cipolla, SegNet: Adeep convolutional encoder-decoder architecture for imagesegmentation, IEEE Transactions on Pattern Analysis andMachine Intelligence, 39(12), 2017, 2481–2495.
  19. [19] T.D. Dung and G. Capi, Application of neural networks forrobot 3D mapping and annotation using depth image camera,International Journal of Robotics & Automation, 37(6), 2022,529–536.
  20. [20] C. Guo, K. Huang, Y. Luo, H. Zhang, and W. Zuo, Object-oriented semantic mapping and dynamic optimization on amobile robot, International Journal of Robotics & Automation,37(4), 2022, 321–331.10
  21. [21] C. Liu, K. Wang, J. Shi, Z. Qiao, and S. Shen, FM-fusion:Instance-aware semantic mapping boosted by vision-languagefoundation models, IEEE Robotics and Automation Letters,9(3), 2024, 2232–2239.
  22. [22] G. Narita, T. Seno, T. Ishikawa, and Y. Kaji, Panopticfusion:Online volumetric semantic mapping at the level of stuffand things, in Proceeding IEEE/RSJ International Conf. onIntelligent Robots and Systems (IROS), Macau, China, 2019,4205–4212.
  23. [23] R. Mascaro, L. Teixeira, and M. Chli, Volumetric instance-levelsemantic mapping via multi-view 2D-to-3D label diffusion,IEEE Robotics and Automation Letters, 7(2), 2022, 3531–3538.
  24. [24] M. Han, Z. Zhang, Z. Jiao, X. Xie, Y. Zhu, S.-C. Zhu, and H.Liu, Reconstructing interactive 3D scenes by panoptic mappingand cad model alignments, in Proceeding IEEE InternationalConf. on Robotics and Automation (ICRA), Xi’an, China,2021, 12199–12206.
  25. [25] Y. Miao, I. Armeni, M. Pollefeys, and D. Barath, Volu-metric semantically consistent 3D panoptic mapping, ArXiv,abs/2309.14737, 2023. Available: https://api.semanticscholar.org/CorpusID:262825282
  26. [26] M. Grinvald, F. Furrer, T. Novkovic, J.J. Chung, C. Cadena,R. Siegwart, and J. Nieto, Volumetric instance-aware semanticmapping and 3D object discovery, IEEE Robotics andAutomation Letters, 4(3), 2019, 3037–3044.
  27. [27] K. Xu, Y. Hao, S. Yuan, C. Wang, and L. Xie, AirVO:An illumination-robust point-line visual odometry, inProceeding IEEE/RSJ International Conf. on IntelligentRobots and Systems (IROS), Detroit, MI, USA, 2023,3429–3436.
  28. [28] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, andS. Su¨sstrunk, SLIC superpixels compared to state-of-the-artsuperpixel methods, IEEE Transactions on Pattern Analysisand Machine Intelligence, 34(11), 2012, 2274–2282.
  29. [29] J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D.Cremers, A benchmark for the evaluation of RGB-D SLAMsystems, in Proceeding IEEE/RSJ International Conf. onIntelligent Robots and Systems, Vilamoura-Algarve, Portugal,2012, 573–580.
  30. [30] A. Handa, T. Whelan, J. McDonald, and A. J. Davison, Abenchmark for RGB-D visual odometry, 3D reconstructionand SLAM, 2014 IEEE International Conf. on Robotics andAutomation (ICRA), Hong Kong, China, 2014, 1524–1531.
  31. [31] Y. Lu and D. Song, Robust RGB-D odometry using point andline features, in Proceedings of the IEEE International Conf.on Computer Vision, Santiago, Chile, 2015, 3934–3942.

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