Abstract
Autonomous shuttle development has gained popularity as one of the research development areas in autonomous vehicle field. In this chapter, the shuttle development in Universiti Malaysia Pahang is highlighted, while its vehicle simulation environment is developed to mimic the real environment of the university which consists of many roundabout junctions. The roundabout environment is constructed in vehicle simulator for data logging and testing and then published to the ROS network. Point cloud matrices from different moving frames are stitched using iterative closest point (ICP) algorithm to form a final useful map for further post-processing. The ICP algorithm performance is shown with different number of stitching frames and the results show that the algorithm is capable to show a reliable single map from different point cloud frames.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
K. Bimbraw, Autonomous cars: Past, present and future. 12th Int. Conf. Informatics Control. Autom. Robot. 01, 191–198 (2015)
M. Salazar, F. Rossi, M. Schiffer, C.H. Onder, M. Pavone, On the interaction between autonomous mobility-on-demand and public transportation systems, in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), (2018), pp. 2262–2269. https://doi.org/10.1109/ITSC.2018.8569381.
U.Z.A. Hamid, S.Z. Ishak, F. Imaduddin, Current landscape of the automotive field in the ASEAN region: Case study of Singapore, Malaysia and Indonesia – a brief overview. Asean J. Automot. Technol. 1(1), 21–28 (2019)
M.A. Zakaria, H. Zamzuri, R. Mamat, S.A. Mazlan, A path tracking algorithm using future prediction control with spike detection for an autonomous vehicle robot. Int. J. Adv. Robot. Syst. 10 (2013). https://doi.org/10.5772/56658
K.A. Zulkepli, H. Zamzuri, M.A.A. Rahman, W.J. Yahya, M. Aizzat, M.Z.A. Zakaria, F.R.A Zakuan, N.H. Faezaa, I-drive: Modular system architecture and hardware configuration for an intelligent vehicle research platform. ARPN J Eng App Sci 12, 4259–4264 (2017)
L.C. Básaca-Preciado et al., Intelligent transportation scheme for autonomous vehicle in smart campus. Proc. IECON 2018 – 44th Annu. Conf. IEEE Ind. Electron. Soc., 3193–3199 (2018). https://doi.org/10.1109/IECON.2018.8592824
P. Marin-Plaza, A. Hussein, D. Martin, A. de la Escalera, iCab use case for ROS-based architecture. Robot. Auton. Syst. 118, 251–262 (2019). https://doi.org/10.1016/j.robot.2019.04.008.
K. Baarath, M.A. Zakaria, M.H. Bin Peeie, U.Z.A. Hamid, A.F.A. Nasir, An investigation on the effect of lateral motion on normal forces acting on each tires for nonholonomic electric vehicle: Experimental results validation, in The IAVSD International Symposium on Dynamics of Vehicles on Roads and Tracks, (2019), pp. 1643–1650
W.K. Chew, M.A. Zakaria, Rover Car Outdoor Localization for Navigation Tracking Using Differential Global Positioning System Estimation. Symp Intell Manuf Mechatron, 535–556 (2019)
R. Miyamoto et al., Vision-based road-following using results of semantic segmentation for autonomous navigation. IEEE Int. Conf. Consum. Electron. – Berlin, ICCE-Berlin 2019, 174–179 (2019). https://doi.org/10.1109/ICCE-Berlin47944.2019.8966198
D. Droeschel, M. Schwarz, S. Behnke, Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner. Robot. Auton. Syst. 88, 104–115 (2017). https://doi.org/10.1016/j.robot.2016.10.017
T. Wan et al., RGB-D point cloud registration via infrared and color camera. Multimed. Tools Appl. 78(23), 33223–33246 (2019). https://doi.org/10.1007/s11042-019-7159-6
S.A.S. Mohamed, M.H. Haghbayan, T. Westerlund, J. Heikkonen, H. Tenhunen, J. Plosila, A survey on Odometry for autonomous navigation systems. IEEE Access 7, 97466–97486 (2019). https://doi.org/10.1109/ACCESS.2019.2929133
X. Zhang, C. Glennie, A. Kusari, LiDAR using a weighted anisotropic iterative closest point algorithm. IEEE J Sel Top Appl Earth Obs Remote Sens 8(7), 3338–3346 (2015)
F.A. Donoso, K.J. Austin, P.R. McAree, How do ICP variants perform when used for scan matching terrain point clouds? Robot. Auton. Syst. 87, 147–161 (2017). https://doi.org/10.1016/j.robot.2016.10.011
H. Liu, T. Liu, Y. Li, M. Xi, T. Li, Y. Wang, Point cloud registration based on MCMC-SA ICP algorithm. IEEE Access 7, 73637–73648 (2019). https://doi.org/10.1109/ACCESS.2019.2919989
R. Marani, V. Renò, M. Nitti, T. D’Orazio, E. Stella, A modified iterative closest point algorithm for 3D point cloud registration. Comput. Civ. Infrastruct. Eng. 31(7), 515–534 (2016). https://doi.org/10.1111/mice.12184
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, V. Koltun, CARLA: An open urban driving simulator. Proc 1st Annu Conf Robot Learn, 1–16 (2017)
C. Crick, G. Jay, S. Osentoski, B. Pitzer, O.C. Jenkins, Rosbridge: Ros for non-ros users, in Robotics Research, (Springer, 2017), pp. 493–504
D. Chetverikov, D. Svirko, D. Stepanov, P. Krsek, The trimmed iterative closest point algorithm. Proc. – Int. Conf. Pattern Recognit. 16(3), 545–548 (2002). https://doi.org/10.1109/icpr.2002.1047997.
Q. Tian, Y.F. Gao, G.L. Li, J.X. Song, A novel global relocalization method based on hierarchical registration of 3D point cloud map for mobile robot. 2019 5th Int. Conf. Control. Autom. Robot. ICCAR 2019, 68–73 (2019). https://doi.org/10.1109/ICCAR.2019.8813720
H. Sobreira et al., Map-matching algorithms for robot self-localization: A comparison between perfect match, iterative closest point and normal distributions transform. J. Intell. Robot. Syst. Theory Appl. 93(3–4), 533–546 (2019). https://doi.org/10.1007/s10846-017-0765-5.
P. Kamencay, M. Sinko, R. Hudec, M. Benco, R. Radil, Improved feature point algorithm for 3D point cloud registration. 2019 42nd Int. Conf. Telecommun. Signal Process. TSP 2019, 517–520 (2019). https://doi.org/10.1109/TSP.2019.8769057
Acknowledgements
The author would like to thank Ministry of Higher EducationMalaysia (KPT) and Universiti Malaysia Pahang(www.ump.edu.my) for financial supports given under FRGS/1/2018/TK08/UMP/02/1, RDU1903139. Not forgotten to Universiti Malaysia Pahang for providing a flagship grant with vote number RDU192201 for the development of the autonomous shuttle prototype inside the campus. Special thanks to all Autonomous Vehicle Laboratory members, research assistants for the data logging and fabrication process.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Zakaria, M.A., Kunjunni, B., Peeie, M.H.B., Papaioannou, G. (2021). Autonomous Shuttle Development at Universiti Malaysia Pahang: LiDAR Point Cloud Data Stitching and Mapping Using Iterative Closest Point Cloud Algorithm. In: Hamid, U.Z.A., Al-Turjman, F. (eds) Towards Connected and Autonomous Vehicle Highways. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-66042-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-66042-0_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-66041-3
Online ISBN: 978-3-030-66042-0
eBook Packages: EngineeringEngineering (R0)