Skip to main content
SpringerLink
Log in

GMSK-SLAM: a new RGB-D SLAM method with dynamic areas detection towards dynamic environments

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

As a research hotspot in the field of robotics, Simultaneous localization and mapping (SLAM) has made great progress in recent years, but few SLAM algorithms take dynamic or movable targets in the scene into account. In this paper, a robust new RGB-D SLAM method with dynamic area detection towards dynamic environments named GMSK-SLAM is proposed. Most of the existing related papers use the method of directly eliminating the whole dynamic targets. Although rejecting dynamic objects can increase the accuracy of robot positioning to a certain extent, this type of algorithm will result in the reduction of the number of available feature points in the image. The lack of sufficient feature points will seriously affect the subsequent precision of positioning and mapping for feature-based SLAM. The proposed GMSK-SLAM method innovatively combines Grid-based Motion Statistics (GMS) feature points matching method with K-means cluster algorithm to distinguish dynamic areas from the images and retain static information from dynamic environments, which can effectively increase the number of reliable feature points and keep more environment features. This method can achieve a highly improvements on localization accuracy in dynamic environments. Finally, sufficient experiments were conducted on the public TUM RGB-D dataset. Compared with ORB-SLAM2 and the RGB-D SLAM, our system, respectively, got 97.3% and 90.2% improvements in dynamic environments localization evaluated by root-mean-square error. The empirical results show that the proposed algorithm can eliminate the influence of the dynamic objects effectively and achieve a comparable or better performance than state-of-the-art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig.6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

Yes

References

  1. 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 

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

    Article  Google Scholar 

  3. Bay H (2006) Surf: speeded up robust features. 9th European Conference on Computer Vision (ECCV 2006), Graz, AUSTRIA, pp 404–417

  4. Bian JW, Lin WY, Matsushita Y (2017) GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence. 30th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp 2828–2837

  5. Calonder M, Lepetit (2010) Brief: binary robust independent elementary features. 11th European Conference on Computer Vision, Heraklion, GREECE, pp 778–792

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

    Article  Google Scholar 

  7. Dissanayake MWMG, Newman P (2013) A solution to the simultaneous localization and map building (slam) problem. IEEE Trans Robot Autom 17(3):229–241

    Article  Google Scholar 

  8. Endres F, Hess J, Engelhard N (2012) An evaluation of the RGB-D SLAM system. IEEE international conference on robotics and automation (ICRA), St Paul, MN, pp 1691-1696

  9. Engel J, Schöps T, Cremers D (2014) Lsd-slam: large-scale direct monocular slam. In: proceedings of European conference on computer vision (ECCV), vol 8690, pp 834-849

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

    Article  Google Scholar 

  11. Fang Y, Dai B (2009) An improved moving target detecting and tracking based on optical flow technique and Kalman filter. 4th International Conference on Computer Science and Education, Nanning, PEOPLES R CHINA, pp 1197–1202

  12. Forster C, Pizzoli M, Scaramuzza D (2014) SVO: Fast Semi-Direct Monocular Visual Odometry. IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, PEOPLES R CHINA, pp 15–22

  13. Harris C G, Stephens M J (1988) A combined corner and edge detector. Proceedings of the 4th Alvey vision conference, Manchester, England, pp 147-151

  14. Hess W, Kohler D, Rapp H (2016) Real-time loop closure in 2D LIDAR SLAM. IEEE international conference on robotics and automation (ICRA), pp 1271-1278

  15. Klein G, Murray D (2007) Parallel tracking and mapping for small AR workspaces. IEEE & Acm International Symposium on Mixed & Augmented Reality.

  16. Kohlbrecher S, Stryk OV, Meyer J (2011) A flexible and scalable SLAM system with full 3D motion estimation. IEEE International Symposium on Safety, Security, and Rescue Robotics, Kyoto, Japan https://doi.org/10.1109/SSRR.2011.6106777

  17. Lee SJ, Hwang SS (2019) Bag of sampled words: a sampling-based strategy for fast and accurate visual place recognition in changing environments. Int J Control Autom Syst 17(10):2597–2609

    Article  Google Scholar 

  18. Li JN, Wang LH, Li Y (2016) Local optimized and scalable frame-to-model SLAM. Multimed Tools Appl 75(14):8675–8694

    Article  Google Scholar 

  19. Liu GH, Zeng WL, Feng B, Xu F (2019) DMS-SLAM: a general visual SLAM system for dynamic scenes with multiple sensors. SENSORS 19(17)

  20. Long J, Shelhamer E, Darrell T (2015) Fully Convolutional Networks for Semantic Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp 3431–3440

  21. Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  22. MacQueen J (1965) Some Methods for Classification and Analysis of Multi-Variate Observations. Proceedings of the Fifth Berkeley Symposium on Math, Statics, and Probability, vol 1, pp 281–297

  23. Mu X, He B, Zhang X (2019) Visual navigation features selection algorithm based on instance segmentation in dynamic environment. IEEE Access 8:465–473

    Article  Google Scholar 

  24. Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. VISAPP, vol 1:331–340

    Google Scholar 

  25. Mur-Artal R, Tardos JD (2017) ORB-SLAM2: an open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Trans Robot 33(5):1255–1262

    Article  Google Scholar 

  26. Mur-Artal R, Montiel JMM, Tardós JD (2015) ORB-SLAM: A Versatile and Accurate Monocular SLAM System. IEEE Trans Robot 31(5):1147–1163

    Article  Google Scholar 

  27. Oh S, Hahn M, Kim J (2015) Dynamic EKF-based SLAM for autonomous mobile convergence platforms. Multimed Tools Appl 74(16):6413–6430

    Article  Google Scholar 

  28. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement.arXiv e-prints,2018

  29. Redmon J, Divvala S, Girshick R, Farhadi A (2015) You only look once: unified, real-time object detection. 2016 IEEE conference on computer vision and pattern recognition (CVPR), Seattle, WA, pp. 779–788

  30. Rosten E, Drummond T (2006) Machine learning for high-speed corner detection. 9th European conference on computer vision (ECCV 2006), Graz, AUSTRIA, pp 430-443

  31. Rublee E, Rabaud V, Konolige K et al (2012) ORB: an efficient alternative to SIFT or SURF. IEEE international conference on computer vision (ICCV), Barcelona, SPAIN, pp 2564-2571

  32. Saputra MRU, Markham A, Trigoni N (2018) Visual SLAM and structure from motion in dynamic environments: a survey. ACM Comput Surv 51(2):1–36

    Article  Google Scholar 

  33. Sharma K (2018) Improved visual SLAM: a novel approach to mapping and localization using visual landmarks in consecutive frames. Multimed Tools Appl 77(7):7955–7976

    Article  Google Scholar 

  34. Smith RC, Cheeseman P (1986) On the representation and estimation of spatial uncertainty. Int J Robot Res 5(4):56–68

    Article  Google Scholar 

  35. Sturm J, Engelhard N, Endres F (2012) A benchmark for the evaluation of RGB-D SLAM systems. 25th IEEE\RSJ International Conference on Intelligent Robots and Systems (IROS), Algarve, PORTUGAL, pp 573–580

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

    Article  Google Scholar 

  37. Tong Q, Peiliang L, Shaojie S (2018) VINS-mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans Robot 34(4):1004–1020

    Article  Google Scholar 

  38. Wang R, Wan W, Wang Y (2019) A new RGB-D SLAM method with moving object detection for dynamic indoor scenes. Remote Sens 11(10)

  39. Wrobel B P (2001) Multiple view geometry in computer vision. Cambrige university press

  40. Yu C, Liu Z, Liu X (2018) DS-SLAM: a semantic visual SLAM towards dynamic environments. 25th IEEE/RSJ international conference on intelligent robots and systems (IROS), Madrid, SPAIN, pp 1168-1174

  41. Zhang W, Chen Q, Zhang W, He X (2018) Long-range terrain perception using convolutional neural networks. Neurocomputing 275:781–787

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported in part by National Natural Science Foundation of China 52071080, Fundamental Research Funds for the Central Universities under Grant 2242021K1G008, Remaining funds cultivation project of National Natural Science Foundation of Southeast University under Grant 9S20172204.

Code available

No (Not applicable).

Author information

Authors and Affiliations

  1. School of Instrument Science & Engineering, Southeast University, Nanjing, 210096, China

    Hongyu Wei, Tao Zhang & Liang Zhang

  2. Key Laboratory of Micro-Inertial Instrument & Advanced Navigation Technology, Ministry of Education, Nanjing, 210096, China

    Hongyu Wei, Tao Zhang & Liang Zhang

Authors
  1. Hongyu Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Liang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Zhang.

Ethics declarations

Conflicts of interest/Competing interests

No

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wei, H., Zhang, T. & Zhang, L. GMSK-SLAM: a new RGB-D SLAM method with dynamic areas detection towards dynamic environments. Multimed Tools Appl 80, 31729–31751 (2021). https://doi.org/10.1007/s11042-021-11168-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11168-5

Keywords

Search

Navigation