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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/111862


    Title: Enhanced Simultaneous Localization and Mapping (ESLAM) for Mobile Robots
    Authors: Hsu, C.C., W.Y. Wang, T.Y. Lin, Y.T. Wang, T.W. Huang
    Keywords: Simultaneous localization and mapping;SLAM;FastSLAM;particle filter;extended Kalman filter;navigation
    Date: 2017-04-13
    Issue Date: 2017-10-27 02:10:49 (UTC+8)
    Abstract: FastSLAM, such as FastSLAM 1.0 and FastSLAM 2.0, is a popular algorithm to solve the simultaneous localization and mapping (SLAM) problem for mobile robots. In real environments, however, the execution speed by FastSLAM would be too slow to achieve the objective of real-time design with a satisfactory accuracy because of excessive comparisons of the measurement with all the existing landmarks in particles, particularly when the number of landmarks is drastically increased. In this paper, an enhanced SLAM (ESLAM) is proposed, which uses not only odometer information but also sensor measurements to estimate the robot’s pose in the prediction step. Landmark information that has the maximum likelihood is then used to update the robot’s pose before updating the landmarks’ location. Compared to existing FastSLAM algorithms, the proposed ESLAM algorithm has a better performance in terms of computation efficiency as well as localization and mapping accuracy as demonstrated in the illustrated examples.
    Relation: International Journal of Humanoid Robotics, vol.14, no.2
    Appears in Collections:[機械與機電工程學系暨研究所] 期刊論文

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