教師資料查詢 | 類別: 期刊論文 | 教師: 王銀添 Wang Yin-tien (瀏覽個人網頁)

標題:Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots
學年105
學期2
出版(發表)日期2017/04/22
作品名稱Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots
作品名稱(其他語言)
著者Hsu, C.C., C.K. Yang, Y.H. Chien, Y.T. Wang, W.Y. Wang, C.H. Chien
單位
出版者
著錄名稱、卷期、頁數Engineering Computations, Vol. 34 Issue: 4, pp.1217-1239
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.

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.
關鍵字SLAM; FastSLAM; Simultaneous localization and mapping; SURF; Visual SLAM
語言英文
ISSN
期刊性質國外
收錄於SCI;EI;
產學合作
通訊作者
審稿制度
國別英國
公開徵稿
出版型式,電子版,紙本
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