Abstract
In this paper, an object-based localization for mobile robot in real-time environments is proposed. The proposed system consists of a mobile platform and LiDAR. The proposed localization algorithm has 4 steps: (1) scanning the point cloud of the environment by the LiDAR mounted on a robot, (2) ground point removal and object segmentation, (3) recognizing objects with Point Feature Histogram (PFH) features, (4) computing the current position and pose by using the geometry relation between the 3D objects. Comparing with SLAM-based systems, the proposed method is more precise and efficient since the map and mapping are not necessary.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Se, S., Lowe, D., Little, J.: Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. Int. J. Robot. Res. 21(8), 735–758 (2002)
Hayet, J., Lerasle, F., Devy, M.: Visual landmarks detection and recognition for mobile robot navigation. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, no. 1, pp. 313–318 (2003)
Wu, C.J., Tsai, W.H.: Localization estimation for indoor autonomous vehicle navigation by omni-directional vision using circular landmarks on ceilings. Robot Auton. Syst. 57(5), 546 (2009)
Irie, K., Yoshida, T., Tomono, M.: Mobile robot localization using stereo vision in outdoor environments under various illumination conditions. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 5175–5181 (2010)
Negenborn, R.: Robot localization and Kalman filters on finding your position in a noisy world. Master’s thesis, Utrecht University, Utrecht, The Netherlands (2003)
Adams, M., Zhang, S., Xie, L.: Particle filter based outdoor robot localization using natural features extracted from laser scanner. In: IEEE International Conference Robot, pp. 854–859 (2004)
Se, S., Lowe, D., Little, J.: Vision-based global localization and mapping for mobile robots. IEEE Trans. Rob. 21, 364–375 (2005)
Lalonde, J.F., Unnikrishnan, R., Vandapel, N., Hebert, M.: Scale Selection for Classification of Point-sampled 3-D Surfaces. In: IEEE International Conference on 3-D Digital Imaging and Modeling, pp. 285–292 (2005)
Pauly, M., Keiser, R., Gross, M.: Multi-scale feature extraction on point-sampled surfaces. Comput. Graph. Forum 22(3), 281–289 (2003)
Rusu, R.B., Blodow, N., Marton, Z.C., Beetz, M.: Aligning point cloud views using persistent feature histograms. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3384–3391 (2008)
Acknowledgment
This work was financially supported by the “Intelligent Recognition Industry Service Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, CC., Huang, LZ., Chiang, HT. (2019). A Localization Approach Based on Fixed 3D Objects for Autonomous Robots. In: Pan, JS., Ito, A., Tsai, PW., Jain, L. (eds) Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2018. Smart Innovation, Systems and Technologies, vol 110. Springer, Cham. https://doi.org/10.1007/978-3-030-03748-2_41
Download citation
DOI: https://doi.org/10.1007/978-3-030-03748-2_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03747-5
Online ISBN: 978-3-030-03748-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)