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Author:

Yu, Naigong (Yu, Naigong.) (Scholars:于乃功) | Zhang, Bo (Zhang, Bo.)

Indexed by:

CPCI-S

Abstract:

In degeneration circumstance such as corridor, due to lacking environmental features, the traditional laser SLAM cannot locate the position of the mobile robot precisely during the scan matching, and further lead the mapping does not confirm to the reality. Aim at this problem, this paper proposes an improved Hector SLAM algorithm based on information fusion for mobile robot. Odometry can accurately reflect the distance of robot movement and Inertial Measurement Unit (IMU) can exactly show the angle of robot movement. So an improved SLAM algorithm framework is set up. In the process of information collection and handling, based on extending Kalman Filtering (EKF) fusing the information of odometry, IMU and laser scanning, to raise the accuracy of the change of point cloud to provide precise input information for scan matching process. In the scanning matching process. Hector SLAM algorithm is used to project the scan information onto grid map. In the last, according to the proposed method, the algorithm function packages are written under the Robot Operating System (ROS). The effectiveness of the method is verified by simulation experiment and robot platform physics experiments.

Keyword:

information fusion mobile robot EKF simultaneous localization and mapping

Author Community:

  • [ 1 ] [Yu, Naigong]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Zhang, Bo]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

Reprint Author's Address:

  • 于乃功

    [Yu, Naigong]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China

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Source :

PROCEEDINGS OF 2018 5TH IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND INTELLIGENCE SYSTEMS (CCIS)

ISSN: 2376-5933

Year: 2018

Page: 279-284

Language: English

Cited Count:

WoS CC Cited Count: 12

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 6

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