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Real-time motion removal based on point correlations for RGB-D SLAM in indoor dynamic environments

  • S.I.: AI based Techniques and Applications for Intelligent IoT Systems
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Abstract

The traditional visual simultaneous localization and mapping (SLAM) systems have a high dependence on static world assumption, which makes them easy to fail to track in dynamic environments. In this paper, we propose a real-time dynamic visual SLAM system (RTDSLAM) based on ORB-SLAM3 to realize accurate pose estimation of the camera in indoor dynamic environments. We regard the static objects in the environments as a complete virtual rigid body and add two motion removal modules to handle the dynamic feature points without the aid of the camera’s ego motion. The geometry-based motion removal module utilizes the point correlations and the structural invariance of rigid body to detect sparse dynamic feature points between two keyframes, and the clustering of depth images helps find the complete dynamic regions. Meanwhile, the template-based motion removal module uses template matching to fast track the known moving objects between ordinary frames. The dynamic feature points located on moving objects are removed and only static feature points are reserved for pose estimation. We evaluate our system on public TUM and Bonn datasets, and the comparison with state-of-the-art dynamic visual SLAM systems shows our advantages both in runtime and the accuracy of pose estimation. Besides, the test in real-world scenes shows the effectiveness of our system in dynamic environments.

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Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Popović G, Cvišić I, Écorchard G et al (2022) Human localization in robotized warehouses based on stereo odometry and ground-marker fusion. Rob Comput Integr Manuf 73:102241

    Article  Google Scholar 

  2. Davison AJ, Reid ID, Molton ND et al (2007) MonoSLAM: real-time single camera SLAM. IEEE Trans Pattern Anal Mach Intell 29(6):1052–1067

    Article  Google Scholar 

  3. Campos C, Elvira R, Rodríguez JJG et al (2021) ORB-SLAM3: an accurate open-source library for visual, visual-inertial, and multimap SLAM. IEEE Trans Rob 37(6):1874–1890

    Article  Google Scholar 

  4. Engel J, Koltun V, Cremers D (2018) Direct sparse odometry. IEEE Trans Pattern Anal Mach Intell 40(3):611–625

    Article  Google Scholar 

  5. Qin T, Li P, Shen S (2018) VINS-Mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans Rob 34(4):1004–1020

    Article  Google Scholar 

  6. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  MathSciNet  Google Scholar 

  7. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495

    Article  Google Scholar 

  8. Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934

  9. He K, Gkioxari G, Dollár P, et al (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision (ICCV), Venice, Italy, pp 2961–2969

  10. Hartigan JA, Wong MA (1979) Algorithm AS 136: a k-means clustering algorithm. J R Stat Soc 28(1):100–108

    MATH  Google Scholar 

  11. Sun Y, Liu M, Meng M et al (2017) Improving RGB-D SLAM in dynamic environments: a motion removal approach. Rob Auton Syst 89:110–122

    Article  Google Scholar 

  12. Tan W, Liu H, Dong Z, et al (2013) Robust monocular SLAM in dynamic environments. In: 2013 IEEE international symposium on mixed and augmented reality (ISMAR), Adelaide, SA, Australia, 2013, pp 209–218

  13. Sun Y, Liu M, Meng MQ-H (2018) Motion removal for reliable RGB-D SLAM in dynamic environments. Rob Auton Syst 108:115–128

    Article  Google Scholar 

  14. Bahraini MS, Bozorg M, Rad AB (2018) SLAM in dynamic environments via ML-RANSAC. Mechatronics 49:105–118

    Article  Google Scholar 

  15. Rousseeuw PJ (1984) Least median of squares regression. J Am Stat Assoc 79(388):871–880

    Article  MathSciNet  MATH  Google Scholar 

  16. Yu C, Liu Z, Liu X-J, et al (2018) DS-SLAM: A semantic visual SLAM towards dynamic environments. In: 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS), Madrid, Spain, pp 1168–1174

  17. Mur-Artal R, Tardós JD (2017) Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans Rob 33(5):1255–1262

    Article  Google Scholar 

  18. Ji T, Wang C, Xie L (2021) Towards real-time semantic RGB-D SLAM in dynamic environments. In: 2021 IEEE international conference on robotics and automation (ICRA), Xi'an, China, pp 11175–11181

  19. Zhong YH, Hu SS, Huang G et al (2022) WF-SLAM: a robust VSLAM for dynamic scenarios via weighted features. IEEE Sens J 22(11):10818–10827

    Article  Google Scholar 

  20. Yang S, Fan G, Bai L et al (2020) SGC-VSLAM: a semantic and geometric constraints VSLAM for dynamic indoor environments. Sensors 20(8):2432

    Article  Google Scholar 

  21. Ma P, Bai Y, Zhu J et al (2019) DSOD: DSO in dynamic environments. IEEE Access 7:178300–178309

    Article  Google Scholar 

  22. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767

  23. Saputra MRU, Markham A, Trigoni N (2019) Visual SLAM and structure from motion in dynamic environments: a survey. ACM Comput Surv 51(2):1–36

    Article  Google Scholar 

  24. Sheng C, Pan SG, Gao W et al (2020) Dynamic-DSO: direct sparse odometry using objects semantic information for dynamic environments. Appl Sci-Basel 10(4):1467

    Article  Google Scholar 

  25. Soares JCV, Gattass M, Meggiolaro MA (2019) Visual SLAM in human populated environments: exploring the trade-off between accuracy and speed of YOLO and Mask R-CNN. In: 2019 19th international conference on advanced robotics (ICAR), Belo Horizonte, Brazil, 2019, pp 135–140

  26. Wu W, Guo L, Gao H et al (2022) YOLO-SLAM: a semantic SLAM system towards dynamic environment with geometric constraint. Neural Comput Appl 34(8):6011–6026

    Article  Google Scholar 

  27. Ding Z, Huang R, Hu B (2019) Robust indoor slam based on pedestrian recognition by using rgb-d camera. In: 2019 Chinese automation congress (CAC), Hangzhou, China, pp 292–297

  28. Bescos B, Facil JM, Civera J et al (2018) DynaSLAM: tracking, mapping, and inpainting in dynamic scenes. IEEE Rob Autom Lett 3(4):4076–4083

    Article  Google Scholar 

  29. Li P, Zhang G, Zhou J, et al (2019) Study on slam algorithm based on object detection in dynamic scene. In: 2019 international conference on advanced mechatronic systems (ICAMechS), Kusatsu, Japan, 2019, pp 363–367

  30. Dai W, Zhang Y, Li P et al (2020) RGB-D SLAM in dynamic environments using point correlations. IEEE Trans Pattern Anal Mach Intell 44(1):373–389

    Article  Google Scholar 

  31. Barber CB, Dobkin DP, Huhdanpaa H (1996) The quickhull algorithm for convex hulls. ACM Trans Math Softw 22(4):469–483

    Article  MathSciNet  MATH  Google Scholar 

  32. Liu G, Zeng W, Feng B et al (2019) DMS-SLAM: a general visual SLAM system for dynamic scenes with multiple sensors. Sensors 19(17):3714

    Article  Google Scholar 

  33. Xie W, Liu PX, Zheng M (2021) Moving object segmentation and detection for robust RGBD-SLAM in dynamic environments. IEEE Trans Instrum Meas 70:1–8

    Google Scholar 

  34. Korman S, Reichman D, Tsur G, et al (2013) Fast-match: fast affine template matching. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2331–2338

  35. Sturm J, Engelhard N, Endres F, et al (2012) A benchmark for the evaluation of RGB-D SLAM systems. In: 2012 IEEE/RSJ international conference on intelligent robots and systems, Vilamoura-Algarve, Portugal, 2012, pp 573–580

  36. Palazzolo E, Behley J, Lottes P, et al (2019) ReFusion: 3D reconstruction in dynamic environments for RGB-D cameras exploiting residuals. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), 2019, pp 7855–7862

  37. Scona R, Jaimez M, Petillot YR, et al (2018) Staticfusion: background reconstruction for dense rgb-d slam in dynamic environments. In: 2018 IEEE international conference on robotics and automation (ICRA), Brisbane, QLD, Australia, 2018, pp 3849–3856

  38. Li S, Lee D (2017) RGB-D SLAM in dynamic environments using static point weighting. IEEE Rob Autom Lett 2(4):2263–2270

    Article  Google Scholar 

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Acknowledgements

This work was supported by Guangdong Basic and Applied Basic Research Foundation (2022A1515010095, 2021A1515010506), the National Natural Science Foundation of China and the Royal Society of Edinburgh (51911530245), the National Natural Science Foundation of China under grant (51675186)

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  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510641, China

    Kesai Wang, Xifan Yao, Nanfeng Ma & Xuan Jing

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  1. Kesai Wang
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  2. Xifan Yao
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  3. Nanfeng Ma
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Correspondence to Xifan Yao.

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Wang, K., Yao, X., Ma, N. et al. Real-time motion removal based on point correlations for RGB-D SLAM in indoor dynamic environments. Neural Comput & Applic 35, 8707–8722 (2023). https://doi.org/10.1007/s00521-022-07879-x

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  • DOI: https://doi.org/10.1007/s00521-022-07879-x

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