Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots

doi:10.1108/EC-05-2015-0123

機構典藏 > College of Engineering > Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering > Journal Article > Item 987654321/111863

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/111863

Title:	Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots
Authors:	Chen-Chien Hsu, Cheng-Kai Yang, Yi-Hsing Chien, Yin-Tien Wang, Wei-Yen Wang, Chiang-Heng Chien
Keywords:	SLAM;FastSLAM;Simultaneous localization and mapping;SURF;Visual SLAM
Date:	2017-06-12
Issue Date:	2017-10-27 02:10:50 (UTC+8)
Abstract:	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. Design/methodology/approach 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. Findings 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. Originality/value 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.
Relation:	Engineering Computations, Vol. 34 No. 4, pp. 1217-1239
DOI:	10.1108/EC-05-2015-0123
Appears in Collections:	[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Journal Article

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