Visual SLAM provides navigation and positioning functions for mobile robots. The accuracy of positioning has a profound impact on the quality of the robot's work. It is of great significance to study SLAM positioning. ORB-SLAM2 is a visual SLAM framework with excellent positioning performance. It uses FAST algorithm as feature detection module. After a large number of experiments, this paper finds that the FAST algorithm has the defect of detecting false corners, and believes that ORB-SLAM2 has room to improve positioning accuracy. After analyzing the causes of false corners, this paper proposes an improved FAST corner detection algorithm, called FAST-Ring corner detection algorithm. The FAST-Ring corner detection algorithm constructs a new feature extraction template, and the point is considered as a corner when it meets the template. In order to verify the effectiveness of the FAST-Ring algorithm, a comparison experiment of feature extraction from a single image and a comparison experiment of ORB-SLAM2 positioning accuracy are performed in this paper. It is experimentally verified that the FAST-Ring algorithm proposed in this paper can filter out some false corner points extracted by the original FAST algorithm, and the average absolute positioning error of ORB-SLAM2 using KITTI dataset is reduced by 5 cm compared with the original ORB-SLAM2. The experimental results prove that the performance of the FAST-Ring algorithm is better than the FAST algorithm.


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    Titel :

    Application of an Improved Fast Corner Detection Algorithm in ORB-SLAM2


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Wu, Meiping (Herausgeber:in) / Niu, Yifeng (Herausgeber:in) / Gu, Mancang (Herausgeber:in) / Cheng, Jin (Herausgeber:in) / Huo, Xiaochuang (Autor:in) / Zhang, Lei (Autor:in) / Liu, Guitao (Autor:in) / Du, Zhenjun (Autor:in) / Liu, Mingmin (Autor:in)

    Kongress:

    International Conference on Autonomous Unmanned Systems ; 2021 ; Changsha, China September 24, 2021 - September 26, 2021



    Erscheinungsdatum :

    2022-03-18


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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