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Improving Processing Time for the Location Algorithm of Robots
Joint Authors
Source
Discrete Dynamics in Nature and Society
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-02
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
The paper proposes an algorithm based on the Multi-State Constraint Kalman Filter (MSCKF) algorithm to construct the map for robots special in the poor GPS signal environment.
We can calculate the position of the robots with the data collected by inertial measurement unit and the features extracted by the camera with MSCKF algorithm in a tight couple way.
The paper focuses on the way of optimizing the position because we adopt it to compute Kalman gain for updating the state of robots.
In order to reduce the processing time, we design a novel fast Gauss–Newton MSCKF algorithm to complete the nonlinear optimization.
Compared with the performance of conventional MSCKF algorithm, the novel fast-location algorithm can reduce the processing time with the kitti datasets.
American Psychological Association (APA)
Chen, Jing& Chen, Liwen. 2020. Improving Processing Time for the Location Algorithm of Robots. Discrete Dynamics in Nature and Society،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1152839
Modern Language Association (MLA)
Chen, Jing& Chen, Liwen. Improving Processing Time for the Location Algorithm of Robots. Discrete Dynamics in Nature and Society No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1152839
American Medical Association (AMA)
Chen, Jing& Chen, Liwen. Improving Processing Time for the Location Algorithm of Robots. Discrete Dynamics in Nature and Society. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1152839
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1152839