Skip to main content

Main menu

  • Home
  • Current Issue
  • Archive
  • About Us
    • About NAVIGATION
    • Editorial Board
    • Peer Review Statement
    • Open Access
  • More
    • Email Alerts
    • Info for Authors
    • Info for Subscribers
  • Other Publications
    • ion

User menu

  • My alerts

Search

  • Advanced search
NAVIGATION: Journal of the Institute of Navigation
  • Other Publications
    • ion
  • My alerts
NAVIGATION: Journal of the Institute of Navigation

Advanced Search

  • Home
  • Current Issue
  • Archive
  • About Us
    • About NAVIGATION
    • Editorial Board
    • Peer Review Statement
    • Open Access
  • More
    • Email Alerts
    • Info for Authors
    • Info for Subscribers
  • Follow ion on Twitter
  • Visit ion on Facebook
  • Follow ion on Instagram
  • Visit ion on YouTube
Research ArticleOriginal Article
Open Access

AGPC-SLAM: Absolute Ground Plane Constrained 3D Lidar SLAM

Weisong Wen and Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation September 2022, 69 (3) navi.527; DOI: https://doi.org/10.33012/navi.527
Weisong Wen
Hong Kong Polytechnic University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Li-Ta Hsu
Hong Kong Polytechnic University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected]
  • Article
  • Figures & Data
  • Supplemental
  • References
  • Info & Metrics
  • PDF
Loading

REFERENCES

  1. ↵
    1. Bai, X.,
    2. Wen, W., &
    3. Hsu, L.-T.
    (2020). Robust visual-inertial integrated navigation system aided by online sensor model adaption for autonomous ground vehicles in urban areas. Remote Sensing, 12(10). https://doi.org/10.3390/rs12101686
  2. ↵
    1. Chang, L.,
    2. Niu, X., &
    3. Liu, T.
    (2020). GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system using IMU/ODO pre-integration. Sensors, 20(17). https://doi.org/10.3390/s20174702
  3. ↵
    1. Choi, S.,
    2. Park, J.,
    3. Byun, J., &
    4. Yu, W.
    (2014). Robust ground plane detection from 3D point clouds. 2014 14th International Conference on Control, Automation and Systems (ICCAS 2014), Gyeonggi-do, South Korea. https://doi.org/10.1109/ICCAS.2014.6987936
  4. ↵
    1. Dill, E. T., &
    2. Uijt de Haag, M.
    (2016). 3D multi-copter navigation and mapping using GPS, inertial, and LiDAR. NAVIGATION, 63(2), 205–220. https://doi.org/10.1002/navi.134
  5. ↵
    1. Dow, J. M.,
    2. Neilan, R. E., &
    3. Rizos, C.
    (2009). The international GNSS service in a changing landscape of global navigation satellite systems. Journal of Geodesy, 83(3), 191–198. http://doi.org/10.1007/s00190-009-0315-4
    CrossRefWeb of Science
  6. ↵
    1. Geiger, A.,
    2. Lenz, P., &
    3. Urtasun, R.
    (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI. https://doi.org/10.1109/CVPR.2012.6248074
  7. ↵
    1. Grisetti, G.,
    2. Kummerle, R.,
    3. Stachniss, C., &
    4. Burgard, W.
    (2010). A tutorial on graph-based SLAM. IEEE Intelligent Transportation Systems Magazine, 2(4), 31–43. https://doi.org/10.1109/MITS.2010.939925
  8. ↵
    1. Grupp, M.
    (2017). evo: Python package for the evaluation of odometry and SLAM. https://github.com/MichaelGrupp/evo
  9. ↵
    1. He, G.,
    2. Yuan, X.,
    3. Zhuang, Y., &
    4. Hu, H.
    (2020). An integrated GNSS/LiDAR-SLAM pose estimation framework for large-scale map building in partially GNSS-denied environments. IEEE Transactions on Instrumentation and Measurement, 70. https://doi.org/10.1109/TIM.2020.3024405
  10. ↵
    1. Hess, W.,
    2. Kohler, D.,
    3. Rapp, H., &
    4. Andor, D.
    (2016). Real-time loop closure in 2D LIDAR SLAM. 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden. https://doi.org/10.1109/ICRA.2016.7487258
  11. ↵
    1. Huang, X.,
    2. Wang, P.,
    3. Cheng, X.,
    4. Zhou, D.,
    5. Geng, Q., &
    6. Yang, R.
    (2019). The ApolloScape open dataset for autonomous driving and its application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(10), 2702–2719. https://doi.org/10.1109/TPAMI.2019.2926463
  12. ↵
    1. Indelman, V.,
    2. Williams, S.,
    3. Kaess, M., &
    4. Dellaert, F.
    (2012). Factor graph based incremental smoothing in inertial navigation systems. 2012 15th International Conference on Information Fusion, Singapore. https://ieeexplore.ieee.org/document/6290565
  13. ↵
    1. Koide, K.,
    2. Miura, J., &
    3. Menegatti, E.
    (2019). A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement, International Journal of Advanced Robotic Systems, 16(2). https://doi.org/10.1177/1729881419841532
  14. ↵
    1. Kuramachi, R.,
    2. Ohsato, A.,
    3. Sasaki, Y., &
    4. Mizoguchi, H.
    (2015). G-ICP SLAM: An odometry-free 3D mapping system with robust 6DoF pose estimation. 2015 IEEE International Conference on Robotics and Biomimetics (ROBIO), Zhuhai, China. https://doi.org/10.1109/ROBIO.2015.7418763
  15. ↵
    1. Li, Q.,
    2. Li, R.,
    3. Ji, K., &
    4. Dai, W.
    (2015). Kalman filter and its application. 2015 8th International Conference on Intelligent Networks and Intelligent Systems (ICINIS), Tianjin, China. https://doi.org/10.1109/ICINIS.2015.35
  16. ↵
    1. Lin, J., &
    2. Zhang, F.
    (2020). Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV. 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France. https://doi.org/10.1109/ICRA40945.2020.9197440
  17. ↵
    1. Low, K.-L.
    (2004). Linear least-squares optimization for point-to-plane ICP surface registration (Technical Report TR04-004). University of North Carolina at Chapel Hill. https://www.comp.nus.edu.sg/~lowkl/publications/lowk_point-to-plane_icp_techrep.pdf
  18. ↵
    1. Ma, L.,
    2. Kerl, C.,
    3. Stückler, J., &
    4. Cremers, D.
    (2016). CPA-SLAM: Consistent plane-model alignment for direct RGB-D SLAM. 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden. https://doi.org/10.1109/ICRA.2016.7487260
  19. ↵
    1. Magnusson, M.,
    2. Andreasson, H.,
    3. Nuchter, A., &
    4. Lilienthal, A. J.
    (2009). Appearance-based loop detection from 3D laser data using the normal distributions transform. 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan. https://doi.org/10.1109/ROBOT.2009.5152712
  20. ↵
    1. Magnusson, M.,
    2. Lilienthal, A., &
    3. Duckett, T.
    (2007). Scan registration for autonomous mining vehicles using 3D-NDT. Journal of Field Robotics: Special Issue on Mining Robotics, 24(10), 803–827. https://doi.org/10.1002/rob.20204
  21. ↵
    1. Mascaro, R.,
    2. Teixeira, L.,
    3. Hinzmann, T.,
    4. Siegwart, R., &
    5. Chli, M.
    (2018). GOMSF: Graph-optimization based multi-sensor fusion for robust UAV pose estimation. 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia. https://doi.org/10.1109/ICRA.2018.8460193
  22. ↵
    1. Pang, S.,
    2. Kent, D.,
    3. Cai, X.,
    4. Al-Qassab, H.,
    5. Morris, D., &
    6. Radha, H.
    (2019). 3D scan registration based localization for autonomous vehicles—A comparison of NDT and ICP under realistic conditions. 2018 IEEE 88th Vehicular Technology Conference, Chicago, IL. https://doi.org/10.1109/VTCFall.2018.8690819
  23. ↵
    1. Qin, T.,
    2. Li, P., &
    3. Shen, S.
    (2018). VINS-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 34(4), 1004–1020. https://doi.org/10.1109/TRO.2018.2853729
  24. ↵
    1. Quigley, M.,
    2. Gerkey, B.,
    3. Conley, K.,
    4. Faust, J.,
    5. Foote, T.,
    6. Leibs, J.,
    7. Berger, E.,
    8. Wheeler, R., &
    9. Ng, A.
    (2009). ROS: An open-source Robot Operating System. ICRA Workshop on Open Source Software. http://www.cim.mcgill.ca/~dudek/417/Papers/quigley-icra2009-ros.pdf
  25. ↵
    1. Saarinen, J.,
    2. Andreasson, H.,
    3. Stoyanov, T., &
    4. Lilienthal, A. J.
    (2013). Normal distributions transform Monte-Carlo localization (NDT-MCL). 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan. https://doi.org/10.1109/IROS.2013.6696380
  26. ↵
    1. Shan, T., &
    2. Englot, B.
    (2018). LeGO-LOAM: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain. https://doi.org/10.1109/IROS.2018.8594299
  27. ↵
    1. Shan, T.,
    2. Englot, B.,
    3. Meyers, D.,
    4. Wang, W.,
    5. Ratti, C., &
    6. Rus, D.
    (2020). LIO-SAM: Tightly-coupled lidar inertial odometry via smoothing and mapping. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV. https://doi.org/10.1109/IROS45743.2020.9341176
  28. ↵
    1. Shetty, A., &
    2. Gao, G. X.
    (2019). Adaptive covariance estimation of LiDAR-based positioning errors for UAVs. NAVIGATION, 66(2), 463–476. https://doi.org/10.1002/navi.307
  29. ↵
    1. Wen, W.,
    2. Bai, X.,
    3. Kan, Y. C., &
    4. Hsu, L.-T.
    (2019a). Tightly coupled GNSS/INS integration via factor graph and aided by fish-eye camera. IEEE Transactions on Vehicular Technology, 68(11), 10651–10662. https://doi.org/10.1109/TVT.2019.2944680
  30. ↵
    1. Wen, W.,
    2. Hsu, L.-T., &
    3. Zhang, G.
    (2018). Performance analysis of NDT-based graph SLAM for autonomous vehicle in diverse typical driving scenarios of Hong Kong. Sensors, 18(11). https://doi.org/10.3390/s18113928
  31. ↵
    1. Wen, W.,
    2. Zhang, G., &
    3. Hsu, L.-T.
    (2020). Object-detection-aided GNSS and its integration with lidar in highly urbanized areas. IEEE Intelligent Transportation Systems Magazine, 12(3), 53–69. https://doi.org/10.1109/MITS.2020.2994131
  32. ↵
    1. Wen, W.,
    2. Zhang, G., &
    3. Hsu, L.-T.
    (2019b). Correcting NLOS by 3D LiDAR and building height to improve GNSS single point positioning. NAVIGATION, 66(4), 705–718. https://doi.org/10.1002/navi.335
  33. ↵
    1. Yang, M. Y., &
    2. Förstner, W.
    (2010). Plane detection in point cloud data (Technical Report No. 1). Institute of Geodesy and Geoinformation at the University of Bonn. http://www.ipb.uni-bonn.de/pdfs/Yang2010Plane.pdf
  34. ↵
    1. Ye, H.,
    2. Chen, Y., &
    3. Liu, M.
    (2019). Tightly coupled 3D lidar inertial odometry and mapping. 2019 International Conference on Robotics and Automation (ICRA), Montreal, Canada. https://doi.org/10.1109/ICRA.2019.8793511
  35. ↵
    1. Zhang, J., &
    2. Singh, S.
    (2014). LOAM: Lidar odometry and mapping in real-time. Robotics: Science and Systems Conference, Berkley, CA. https://doi.org/10.15607/RSS.2014.X.007
  36. ↵
    1. Zhang, J., &
    2. Singh, S.
    (2017). Low-drift and real-time lidar odometry and mapping. Autonomous Robots, 41(2), 401–416. https://doi.org/10.1007/s10514-016-9548-2
  37. ↵
    1. Zhao, S.,
    2. Fang, Z.,
    3. Li, H., &
    4. Scherer, S.
    (2019). A robust laser-inertial odometry and mapping method for large-scale highway environments. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China. https://doi.org/10.1109/IROS40897.2019.8967880
  38. ↵
    1. Zheng, L.,
    2. Zhu, Y.,
    3. Xue, B.,
    4. Liu, M., &
    5. Fan, R.
    (2019). Low-cost GPS-aided lidar state estimation and map building. 2019 IEEE International Conference on Imaging Systems and Techniques (IST), Abu Dhabi, United Arab Emirates. https://doi.org/10.1109/IST48021.2019.9010530
  39. ↵
    1. Zuo, X.,
    2. Geneva, P.,
    3. Lee, W.,
    4. Liu, Y., &
    5. Huang, G.
    (2019). LIC-Fusion: LiDAR-inertial-camera odometry. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China. https://doi.org/10.1109/IROS40897.2019.8967746
PreviousNext
Back to top

In this issue

NAVIGATION: Journal of the Institute of Navigation: 69 (3)
NAVIGATION: Journal of the Institute of Navigation
Vol. 69, Issue 3
Fall 2022
  • Table of Contents
  • Index by author
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on NAVIGATION: Journal of the Institute of Navigation.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
AGPC-SLAM: Absolute Ground Plane Constrained 3D Lidar SLAM
(Your Name) has sent you a message from NAVIGATION: Journal of the Institute of Navigation
(Your Name) thought you would like to see the NAVIGATION: Journal of the Institute of Navigation web site.
Citation Tools
AGPC-SLAM: Absolute Ground Plane Constrained 3D Lidar SLAM
Weisong Wen, Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation Sep 2022, 69 (3) navi.527; DOI: 10.33012/navi.527

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
AGPC-SLAM: Absolute Ground Plane Constrained 3D Lidar SLAM
Weisong Wen, Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation Sep 2022, 69 (3) navi.527; DOI: 10.33012/navi.527
Reddit logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
    • Abstract
    • 1 INTRODUCTION
    • 2 RELATED WORK
    • 3 OVERVIEW OF THE PROPOSED METHOD
    • 4 METHODOLOGY
    • 5 EXPERIMENT RESULTS AND DISCUSSIONS
    • 6 CONCLUSION AND FUTURE WORK
    • HOW TO CITE THIS ARTICLE
    • APPENDIX: PERFORMANCE OF AGPC-SLAM AT 6 GCPS
    • REFERENCES
  • Figures & Data
  • Supplemental
  • References
  • Info & Metrics
  • PDF

Related Articles

  • Google Scholar

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • GPS Spoofing Mitigation and Timing Risk Analysis in Networked Phasor Measurement Units via Stochastic Reachability
  • A Consistent Regional Vertical Ionospheric Model and Application in PPP-RTK Under Sparse Networks
  • Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning
Show more Original Article

Similar Articles

Keywords

  • dynamic object
  • ground plane constraint
  • lidar SLAM
  • urban canyons

Unless otherwise noted, NAVIGATION content is licensed under a Creative Commons CC BY 4.0 License.

© 2023 The Institute of Navigation, Inc.

Powered by HighWire