Skip to main content
Springer Nature Link
Log in

A novel loop closure detection algorithm based on crossroad scenes

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Loop closure detection (LCD) is crucial for simultaneous localization and mapping (SLAM). Current LiDAR-based methods focus on global scenes and often overlook the rich geometric features of crossroads. These scenes, with their irregular contours, traffic facilities, and vehicles, provide valuable information that is not adequately captured by single-dimensional descriptors, leading to weak discriminative ability. To address this, a novel descriptor called singular value decomposition scan context (SVDSC) is proposed, leveraging singular value decomposition (SVD) to extract geometric features of crossroads, enhancing recognition capability. An adaptive weighted similarity calculation method is also introduced to improve accuracy by considering local feature values. Experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI), Jilin University (JLU), and self-collected crossroad datasets demonstrate the method’s superior performance in complex scenarios. Integrating this LCD algorithm into SLAM yields better mapping outcomes.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Algorithm 1
Fig. 6
Fig. 7
Algorithm 2
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data availability

No datasets were generated or analysed during the current study.

References

  1. Shan T, Englot B (2018) Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4758–4765. IEEE

  2. Wang H, Wang C, Chen C-L, Xie L (2021) F-loam: Fast lidar odometry and mapping. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4390–4396. IEEE

  3. Zhang K, Li Z, Ma J (2021) Appearance-based loop closure detection via bidirectional manifold representation consensus. In: 2021 IEEE International Conference on Robotics and Automation (ICRA), pp. 6811–6817. IEEE

  4. Xu J, Yan N, Tang F (2022) An improvement of loop closure detection based on bow for ratslam. In: 2022 37th Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 634–639. IEEE

  5. Osman H, Darwish N, Bayoumi A (2023) Placenet: a multi-scale semantic-aware model for visual loop closure detection. Eng Appl Artif Intell 119:105797. https://doi.org/10.1016/j.engappai.2022.105797

    Article  Google Scholar 

  6. Jin S, Dai X, Meng Q (2023) Loop closure detection with patch-level local features and visual saliency prediction. Eng Appl Artif Intell 120:105902. https://doi.org/10.1016/j.engappai.2023.105902

    Article  Google Scholar 

  7. Kim G, Kim A (2018) Scan context: egocentric spatial descriptor for place recognition within 3d point cloud map. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4802–4809. IEEE

  8. Wang Y, Sun Z, Xu C-Z, Sarma SE, Yang J, Kong H (2020) Lidar iris for loop-closure detection. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5769–5775. IEEE

  9. Xu D, Liu J, Liang Y, Lv X, Hyyppä J (2022) A lidar-based single-shot global localization solution using a cross-section shape context descriptor. ISPRS J Photogramm Remote Sens 189:272–288

    Article  Google Scholar 

  10. Wang H, Wang C, Xie L (2020) Intensity scan context: Coding intensity and geometry relations for loop closure detection. In: 2020 IEEE International Conference on Robotics and Automation (ICRA), pp. 2095–2101. https://doi.org/10.1109/ICRA40945.2020.9196764

  11. Zhou R, He L, Zhang H, Lin X, Guan Y (2022) Ndd: a 3d point cloud descriptor based on normal distribution for loop closure detection. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1328–1335. https://doi.org/10.1109/IROS47612.2022.9981180

  12. Wang J, Tian B, Zhang R, Chen L (2022) Ulsm: underground localization and semantic mapping with salient region loop closure under perceptually-degraded environment. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1320–1327. IEEE

  13. Tan J, Torroba I, Xie Y, Folkesson J (2023) Data-driven loop closure detection in bathymetric point clouds for underwater slam. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 3131–3137. https://doi.org/10.1109/ICRA48891.2023.10160783

  14. Rusu RB, Blodow N, Marton ZC, Beetz M (2008) Aligning point cloud views using persistent feature histograms. In: 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3384–3391. https://doi.org/10.1109/IROS.2008.4650967

  15. Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (fpfh) for 3d registration. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3212–3217. IEEE

  16. Salti S, Tombari F, Di Stefano L (2014) Shot: unique signatures of histograms for surface and texture description. Comput Vis Image Underst 125:251–264

    Article  Google Scholar 

  17. Cui Y, Zhang Y, Dong J, Sun H, Chen X, Zhu F (2024) Link3d: Linear keypoints representation for 3d lidar point cloud. IEEE Robot Autom Lett 9(3):2128–2135. https://doi.org/10.1109/LRA.2024.3354550

    Article  Google Scholar 

  18. Zhao Huan, Tang Minjie, Ding Han (2020) HoPPF: a novel local surface descriptor for 3D object recognition. Pattern Recognit 103:107272. https://doi.org/10.1016/j.patcog.2020.107272

    Article  Google Scholar 

  19. Rizzini DL (2017) Place recognition of 3d landmarks based on geometric relations. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 648–654. https://doi.org/10.1109/IROS.2017.8202220

  20. Ao S, Hu Q, Yang B, Markham A, Guo Y (2021) Spinnet: learning a general surface descriptor for 3d point cloud registration. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11748–11757. https://doi.org/10.1109/CVPR46437.2021.01158

  21. Wohlkinger W, Vincze M (2011) Ensemble of shape functions for 3d object classification. In: 2011 IEEE International Conference on Robotics and Biomimetics, pp. 2987–2992. https://doi.org/10.1109/ROBIO.2011.6181760

  22. He L, Wang X, Zhang H (2016) M2dp: a novel 3d point cloud descriptor and its application in loop closure detection. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 231–237. https://doi.org/10.1109/IROS.2016.7759060

  23. Deng H, Pei Z, Tang Z, Zhang J, Yang J (2023) Fusion scan context: a global descriptor fusing altitude, intensity and density for place recognition. In: 2023 IEEE International Conference on Mechatronics and Automation (ICMA), pp. 1604–1610. https://doi.org/10.1109/ICMA57826.2023.10215550

  24. Li L, Kong X, Zhao X, Huang T, Li W, Wen F, Zhang H, Liu Y (2021) Ssc: semantic scan context for large-scale place recognition. In: 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2092–2099. https://doi.org/10.1109/IROS51168.2021.9635904

  25. Fan Y, Du X, Luo L, Shen J (2022) Fresco: frequency-domain scan context for lidar-based place recognition with translation and rotation invariance. In: 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), pp. 576–583. https://doi.org/10.1109/ICARCV57592.2022.10004331

  26. Kim G, Choi S, Kim A (2022) Scan context++: structural place recognition robust to rotation and lateral variations in urban environments. IEEE Trans Rob 38(3):1856–1874. https://doi.org/10.1109/TRO.2021.3116424

    Article  Google Scholar 

  27. Jiang B, Shen S (2023) Contour context: abstract structural distribution for 3d lidar loop detection and metric pose estimation. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 8386–8392. https://doi.org/10.1109/ICRA48891.2023.10160337

  28. Bueso D, Piles M, Camps-Valls G (2018) Nonlinear complex pca for spatio-temporal analysis of global soil moisture. In: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Ssensing Symposium, pp. 5780–5783. https://doi.org/10.1109/IGARSS.2018.8518155

  29. Almeida MC, Asada EN, Garcia AV (2008) On the use of gram matrix in observability analysis. IEEE Trans Power Syst 23(1):249–251. https://doi.org/10.1109/TPWRS.2007.913731

    Article  Google Scholar 

  30. Gang W, Xiaomeng W, Yu C, Tongzhou Z, Minghui H, Zhaohan L (2022) A multi-channel descriptor for LiDAR-based loop closure detection and its application. Remote Sens 14(22):5877

    Article  Google Scholar 

  31. Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? the kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354–3361. https://doi.org/10.1109/CVPR.2012.6248074

  32. Gang W, Xudong J, Wei Z, Yu C, Hao Z (2022) 3PCD-TP: a 3D point cloud descriptor for loop closure detection with twice projection. Remote Sens 15(1):82

    Article  Google Scholar 

  33. Yongzhe C, Gang W, Wei Z, Tongzhou Z, Hao Z (2023) A localization algorithm based on global descriptor and dynamic range search. Remote Sens 15(5):1190

    Article  Google Scholar 

  34. Gang W, Xinyu G, Tongzhou Z, Qian X, Wei Z (2022) LiDAR Information Constraints for Rugged odometry and mapping based on neighborhood terrain. Remote Sens 14(20):5229

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Longfei Zhang, Gang Wang & Wei Zhou

  2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Gang Wang

  3. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130012, China

    Gang Wang

Authors
  1. Longfei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z.L. wrote the main manuscript text and prepared figures 1–19. All authors reviewed the manuscript. Z.W. helped to design the experiment and checked the manuscript. W.G. helped to design the first idea of the manuscript.

Corresponding author

Correspondence to Gang Wang.

Ethics declarations

Conflict of interest

The authors declare no conflict interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Wang, G. & Zhou, W. A novel loop closure detection algorithm based on crossroad scenes. J Supercomput 81, 67 (2025). https://doi.org/10.1007/s11227-024-06488-w

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11227-024-06488-w

Keywords