Skip to main content

Correction Algorithm of LIDAR Data for Mobile Robots

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10463))

Included in the following conference series:

Abstract

Laser range finder (LRF) or laser distance sensor (LDS), further referred to as LIDAR (light detection and ranging). LIDAR can obtain environmental point cloud data, while a robot can realize environmental sensing by adoption of the point cloud data generated and LIDAR-based SLAM (Simultaneous Localization And Mapping) algorithm. The precision of point clouds provided by the LIDAR determines that of environmental sensing of the LIDAR-based mobile robot. In this paper, a common correction algorithm has been proposed to correct the inaccuracy of measured point cloud data caused by mobile LIDAR, effectively improving the precision of point cloud data measured by the LIDAR under a mobile state. It also conducts mathematical derivation of the algorithm, presents simulation and real world experiments performed and verifies the necessity and effectiveness of the algorithm derived by experimental results in the paper.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Hokuyo Automation: Scanning range finder, distance data output type for robotics (2017). http://www.hokuyo-aut.jp

  2. SICK: Detection and ranging solutions (2017). https://www.sick.com

  3. Konolige, K., Augenbraun, J., Donaldson, N., Fiebig, C., Shah, P.: A low-cost laser distance sensor. In: IEEE International Conference on Robotics and Automation, pp. 3002–3008 (2008)

    Google Scholar 

  4. Wehr, A., Lohr, U.: Airborne laser scanning-an introduction and overview. ISPRS J. Photogramm. Remote Sens. 54, 68–82 (1999)

    Article  Google Scholar 

  5. Baltsavias, E.P.: Airborne laser scanning: existing systems and firms and other resources. ISPRS J. Photogramm. Remote Sens. 54(2–3), 164–198 (1999)

    Article  Google Scholar 

  6. Montemerlo, M., Thrun, S.: Large-scale robotic 3-D mapping of urban structures. In: ISER, Singapore (2004)

    Google Scholar 

  7. Kohlbrecher, S., von Stryk, O., Meyer, J., Klingauf, U.: A flexible and scalable SLAM system with full 3D motion estimation. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, Kyoto, Japan, September 2011

    Google Scholar 

  8. Hess, W., Kohler, D., Rapp, H., Andor, D.: Real-time loop closure in 2D LIDAR SLAM. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1271–1278 (2016)

    Google Scholar 

  9. The Contributors of the Robot Operating System (ROS): LaserScan Message (2017). http://docs.ros.org/api/sensor_msgs/html/msg/LaserScan.html

  10. Corke, P.: Robotics, Vision and Control-Fundamental Algorithms in MATLAB, vol. 73. Springer, Heidelberg (2011). pp. 32–40

    Book  MATH  Google Scholar 

  11. INMOTION ROBOT: 2D LiDAR product (2017). https://robot.imscv.com

Download references

Acknowledgments

The authors thank National Engineering Research Center of Manufacturing Equipment Digitization and State Key Laboratory of Material Processing and Die & Mould Technology for supporting our work. Thank INMOTION ROBOT and Hokuyo Automation Co., LTD for providing LIDAR.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gen Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bai, W., Li, G., Han, L. (2017). Correction Algorithm of LIDAR Data for Mobile Robots. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10463. Springer, Cham. https://doi.org/10.1007/978-3-319-65292-4_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-65292-4_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65291-7

  • Online ISBN: 978-3-319-65292-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics