SpringerLink
Log in

Detecting and tracking a road-drivable area with three-dimensional point clouds and IoT for autonomous applications

  • Special Issue Paper
  • Published:
Service Oriented Computing and Applications Aims and scope Submit manuscript

Abstract

This work presents a Light Detection and Ranging (LiDAR)-based point cloud method for detecting and tracking road edges. Initially, this work explores the progress in detecting road curb issues. A dataset (called PandaSet) with a Pandar64 sensor to capture different city scenes is used. LiDAR point cloud, as part of an IoT ecosystem, detects the road curb and requires distinguishing the right and left road curbs with regard to the ego car. The curb point’s features use Random Sample Consensus (RANSAC)-based polynomial quadratic approximation to obtain the prospect curb points to eliminate false positive ones. Through extensive experiments, we demonstrate the effectiveness and reliability of our method under various traffic and environmental conditions. Our results showcase a maximum drift of 1.62 m for left curb points and 0.87 m for right curb points, highlighting the superior accuracy and stability of our approach. This LiDAR-based curb detection framework paves the way for enhanced lane recognition and path planning in autonomous driving applications.

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
EUR 32.99 /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 includes VAT (Canada)

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

Similar content being viewed by others

References

  1. Hütt C, Bolten A, Hüging H, Bareth G (2023) UAV LiDAR metrics for monitoring crop height, biomass and nitrogen uptake: a case study on a winter wheat field trial. PFG J Photogramm Remote Sens Geoinform Sci. https://doi.org/10.1007/s41064-022-00228-6

  2. Zhang Y, Zou S, Liu X, Huang X, Wan Y, Yao Y (2022) LiDAR-guided stereo matching with a spatial consistency constraint. ISPRS J Photogramm Remote Sens. https://doi.org/10.1016/j.isprsjprs.2021.11.003

    Article  Google Scholar 

  3. Santos MF, Victorino AC and Pousseur H (2023) Model-based and machine learning-based high-level controller for autonomous vehicle navigation: lane centering and obstacles avoidance. IAES Int J Robot Autom. https://doi.org/10.11591/ijra.v12i1.pp84-97

  4. Suleymanov T, Kunze L and Newman P (2019) Online inference and detection of curbs in partially occluded scenes with sparse LIDAR. In: 2019 IEEE Intelligent Transportation Systems Conference, ITSC 2019. https://doi.org/10.1109/ITSC.2019.8917086

  5. Horváth E, Pozna C, Unger M (2022) Real-time lidar-based urban road and sidewalk detection for autonomous vehicles. Sensors. https://doi.org/10.3390/s22010194

    Article  Google Scholar 

  6. Bashi OID, Hameed HK, Al Kubaiaisi YM and Sabry AH (2023) Development of object detection from point clouds of a 3d dataset by point-pillars neural network. Eastern-Eur J Enterprise Technol. https://doi.org/10.15587/1729-4061.2023.275155

  7. Shallal AH, Salman SA, Sabry AH (2022) Hall sensor-based speed control of a 3-phase permanent-magnet synchronous motor using a field-oriented algorithm. Indones J Electr Eng Comput Sci 27(3):1366–1374. https://doi.org/10.11591/ijeecs.v27.i3.pp1366-1374

    Article  Google Scholar 

  8. Sun PP, Zhao XM, Xu ZG, Min HG (2018) Urban curb robust detection algorithm based on 3D-LIDAR. Zhejiang Daxue Xuebao (Gongxue Ban)/J Zhejiang Univ (Eng Sci). https://doi.org/10.3785/j.issn.1008-973X.2018.03.012

    Article  Google Scholar 

  9. Panev S, Vicente F, De La Torre F, Prinet V (2019) Road curb detection and localization with monocular forward-view vehicle camera. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2018.2878652

    Article  Google Scholar 

  10. Yang B, Liu Y, Dong Z, Liang F, Li B, Peng X (2017) 3D local feature BKD to extract road information from mobile laser scanning point clouds. ISPRS J Photogramm Remote Sens. https://doi.org/10.1016/j.isprsjprs.2017.06.007

    Article  Google Scholar 

  11. Zhang Y, Wang J, Wang X, Dolan JM (2018) Road-segmentation-based curb detection method for self-driving via a 3D-LiDAR sensor. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2018.2789462

    Article  Google Scholar 

  12. Huang R, Chen J, Liu J, Liu L, Yu B, Wu Y (2017) A practical point cloud based road curb detection method for autonomous vehicle. Information (Switzerland). https://doi.org/10.3390/info8030093

    Article  Google Scholar 

  13. Liu T, Wang Y, Niu X, Chang L, Zhang T, Liu J (2022) LiDAR odometry by deep learning-based feature points with two-step pose estimation. Remote Sens. https://doi.org/10.3390/rs14122764

    Article  Google Scholar 

  14. Wang M, Liu R, Lu X, Ren H, Chen M, Yu J (2020) The use of mobile lidar data and Gaofen-2 image to classify roadside trees. Meas Sci Technol. https://doi.org/10.1088/1361-6501/aba322

    Article  Google Scholar 

  15. Kim DH, Kim GW (2021) Automatic multiple LiDAR calibration based on the plane features of structured environments. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3087266

    Article  Google Scholar 

  16. Lv X, Wang S, Ye D (2021) CFNet: LiDAR-camera registration using calibration flow network. Sensors. https://doi.org/10.3390/s21238112

    Article  Google Scholar 

  17. ** the topographic features of mining-related valley fills using mask R-CNN deep learning and digital elevation data. Remote Sens. https://doi.org/10.3390/rs12030547

    Article  Google Scholar 

  18. Wang G, Wu J, He R, Tian B (2021) Speed and accuracy tradeoff for LiDAR data based road boundary detection. IEEE/CAA J Autom Sinica. https://doi.org/10.1109/JAS.2020.1003414

    Article  Google Scholar 

  19. Hu J, Razdan A, Femiani JC, Cui M, Wonka P (2007) Road network extraction and intersection detection from aerial images by tracking road footprints. In IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2007.906107

    Article  Google Scholar 

  20. Byun J, Sung J, Roh MC, Kim SH (2011) Autonomous driving through Curb detection and tracking. In: URAI 2011 - 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence. https://doi.org/10.1109/URAI.2011.6145975

  21. Sun P, Zhao X, Xu Z, Wang R, Min H (2019) A 3D LiDAR data-based dedicated road boundary detection algorithm for autonomous vehicles. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2902170

    Article  Google Scholar 

  22. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-net: Learning dense volumetric segmentation from sparse annotation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). https://doi.org/10.1007/978-3-319-46723-8_49

  23. Jiang F, Gao H, Qiu S, Zhang H, Wan R, Pu J (2023) Knowledge distillation from 3D to bird’s-eye-view for LiDAR semantic segmentation. In Proceedings - IEEE International Conference on Multimedia and Expo. https://doi.org/10.1109/ICME55011.2023.00076

Download references

Author information

Author notes

  1. Mahmood H. Enad and Asaad A. Alhasoon have contributed equally.

Authors and Affiliations

  1. Department of Electrical Techniques, Technical Institute of Karbala, Al-Furat Al-Awsat Technical University, Najaf, Iraq

    Mahmood H. Enad

  2. Medical Technical Institute, Northern Technical University, Mosul, Iraq

    Omar I. Dallal Bashi

  3. Iraqi Commission for Computers and Informatics, Baghdad, 10009, Iraq

    Shymaa Mohammed Jameel

  4. Ministry of Scientific and Technology Research, Jadrriyah, Baghdad, Iraq

    Asaad A. Alhasoon

  5. Department of Sustainability Management, Dubai Academic Health Corporation, Dubai, UAE

    Yasir Mahmood Al Kubaisi

  6. Department of Telecommunications and Information, High Institute of Telecommunications and Post, Baghdad, Iraq

    Husamuldeen K. Hameed

Authors
  1. Mahmood H. Enad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omar I. Dallal Bashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shymaa Mohammed Jameel
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Asaad A. Alhasoon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yasir Mahmood Al Kubaisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Husamuldeen K. Hameed
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Mahmood H. Enad or Asaad A. Alhasoon.

Ethics declarations

Conflict of interest

The authors have no conflicts of interest to declare.

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

Enad, M.H., Bashi, O.I.D., Jameel, S.M. et al. Detecting and tracking a road-drivable area with three-dimensional point clouds and IoT for autonomous applications. SOCA (2024). https://doi.org/10.1007/s11761-024-00399-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11761-024-00399-7

Keywords

Search

Navigation