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    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/111863

    题名: Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots
    作者: Chen-Chien Hsu, Cheng-Kai Yang, Yi-Hsing Chien, Yin-Tien Wang, Wei-Yen Wang, Chiang-Heng Chien
    关键词: SLAM;FastSLAM;Simultaneous localization and mapping;SURF;Visual SLAM
    日期: 2017-06-12
    上传时间: 2017-10-27 02:10:50 (UTC+8)
    摘要: Purpose
    FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in each particle. As a result, the execution speed will be too slow to achieve the objective of real-time navigation. Thus, this paper aims to improve the computational efficiency and estimation accuracy of conventional SLAM algorithms.

    As an attempt to solve this problem, this paper presents a computationally efficient SLAM (CESLAM) algorithm, where odometer information is considered for updating the robot’s pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates.

    Simulation results show that the proposed CESLAM can overcome the problem of heavy computational burden while improving the accuracy of localization and mapping building. To practically evaluate the performance of the proposed method, a Pioneer 3-DX robot with a Kinect sensor is used to develop an RGB-D-based computationally efficient visual SLAM (CEVSLAM) based on Speeded-Up Robust Features (SURF). Experimental results confirm that the proposed CEVSLAM system is capable of successfully estimating the robot pose and building the map with satisfactory accuracy.

    The proposed CESLAM algorithm overcomes the problem of the time-consuming process because of unnecessary comparisons in existing FastSLAM algorithms. Simulations show that accuracy of robot pose and landmark estimation is greatly improved by the CESLAM. Combining CESLAM and SURF, the authors establish a CEVSLAM to significantly improve the estimation accuracy and computational efficiency. Practical experiments by using a Kinect visual sensor show that the variance and average error by using the proposed CEVSLAM are smaller than those by using the other visual SLAM algorithms.
    關聯: Engineering Computations, Vol. 34 No. 4, pp. 1217-1239
    DOI: 10.1108/EC-05-2015-0123
    显示于类别:[機械與機電工程學系暨研究所] 期刊論文


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