Abstract
FastSLAM algorithm is one of the introduced Simultaneous Localization and Mapping (SLAM) algorithms for autonomous mobile robot. It decomposes the SLAM problem into one distinct localization problem and a collection of landmarks estimation problems. In recent discovery, FastSLAM suffers particle depletion problem which causes it to degenerate over time in terms of accuracy. In this work, a new hybrid approach is proposed by integrating two soft computing techniques that are genetic algorithm (GA) and particle swarm optimization (PSO) into FastSLAM. It is developed to overcome the particle depletion problem occur by improving the FastSLAM accuracy in terms of robot and landmark set position estimation. The experiment is conducted in simulation where the result is evaluated using root mean square error (RMSE) analysis. The experiment result shows that the proposed hybrid approach able to minimize the FastSLAM problem by reducing the degree of error occurs (RMSE value) during robot and landmark set position estimation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: a factored solution to the simultaneous localization and mapping problem. In: 18th National Conference on Artificial Intelligence, Menlo Park, CA, USA, pp. 593–598 (2002)
Montemerlo, M., Thrun, S.: FastSLAM, vol. 27, 1st edn. Springer, Heidelberg (2007)
Bailey, T., Nieto, J., Nebot, E.: Consistency of the FastSLAM algorithm. In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 424–429 (2006)
Xia, Y.-M., Yang, Y.-M.: An improved FastSLAM algorithm based on genetic algorithms. In: Qi, L. (ed.) ISIA 2010. CCIS, vol. 86, pp. 296–302. Springer, Heidelberg (2011). doi:10.1007/978-3-642-19853-3_43
Lee, H.-C., Park, S.-K., Choi, J.-S., Lee, B.-H.: PSO-FastSLAM: an improved FastSLAM framework using particle swarm optimization. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 2763–2768 (2009)
Gongyuan, Z., Yongmei, C., Feng, Y., Quan, P.: Particle filter based on PSO. In: International Conference on Intelligent Computation Technology and Automation, pp. 121–124 (2008)
Zou, Z., Cai, Z., Chen, B.: An improved FastSLAM method based on niche technique and particle swarm optimization. In: Control and Decision Conference, China, pp. 2414–2418 (2011)
Choi, M., Sakthivel, R., Chung, W.K.: Neural network-aided extended Kalman filter for SLAM problem. In: Conference on Robotics and Automation, Italy, pp. 1686–1690 (2007)
Dai, X.F., Hao, B., Shao, L.: Self-organizing neural networks for simultaneous localization and mapping of indoor mobile robots. In: Intelligent Networks and Intelligent Systems, China, pp. 115–118 (2008)
Mahrani, M.: Hybrid genetic algorithm and particle swarm optimization model for simultaneous localization and mapping problem. Ph.D. thesis, Universiti Teknologi Malaysia (2016)
Zhang, H., Dai, X.: Soft computing technique for simultaneous localization and mapping of mobile robots. In: International Conference on E-Product, E-Service and E-Entertainment (ICEEE), Henan, China, pp. 1–4 (2010)
Bailey, T.: SLAM Simulation Toolbox, 3 April 2015. http://wwwpersonal.acfr.usyd.edu.au/tbailey/software/slam_simulations.htm
Acknowledgment
This work was supported by Fundamental Research Grant Scheme (FRGS) under grant number R.J130000.7828.4F860 funded by Ministry of Education (MOHE) under the Malaysian government for Faculty of Computing, Universiti Teknologi Malaysia (UTM).
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
Khairuddin, A.R., Talib, M.S., Haron, H., Abdullah, M.Y.C. (2017). GA-PSO-FASTSLAM: A Hybrid Optimization Approach in Improving FastSLAM Performance. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-53480-0_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-53479-4
Online ISBN: 978-3-319-53480-0
eBook Packages: EngineeringEngineering (R0)