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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Hokuyo Automation: Scanning range finder, distance data output type for robotics (2017). http://www.hokuyo-aut.jp
SICK: Detection and ranging solutions (2017). https://www.sick.com
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)
Wehr, A., Lohr, U.: Airborne laser scanning-an introduction and overview. ISPRS J. Photogramm. Remote Sens. 54, 68–82 (1999)
Baltsavias, E.P.: Airborne laser scanning: existing systems and firms and other resources. ISPRS J. Photogramm. Remote Sens. 54(2–3), 164–198 (1999)
Montemerlo, M., Thrun, S.: Large-scale robotic 3-D mapping of urban structures. In: ISER, Singapore (2004)
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
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)
The Contributors of the Robot Operating System (ROS): LaserScan Message (2017). http://docs.ros.org/api/sensor_msgs/html/msg/LaserScan.html
Corke, P.: Robotics, Vision and Control-Fundamental Algorithms in MATLAB, vol. 73. Springer, Heidelberg (2011). pp. 32–40
INMOTION ROBOT: 2D LiDAR product (2017). https://robot.imscv.com
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
Corresponding author
Editor information
Editors and Affiliations
Rights 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)