Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature Fusion
Traditional wheeled robot vision algorithms suffer from low texture tracking failures. Therefore, this study proposes a vision improvement algorithm for mobile robots in view of multi feature fusion; This algorithm introduces line surface features and Manhattan Frame on the basis of traditional algo...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10251515/ |
| _version_ | 1797676678819872768 |
|---|---|
| author | Xiaomei Hu Luying Zhu Ping Wang Haili Yang Xuan Li |
| author_facet | Xiaomei Hu Luying Zhu Ping Wang Haili Yang Xuan Li |
| author_sort | Xiaomei Hu |
| collection | DOAJ |
| description | Traditional wheeled robot vision algorithms suffer from low texture tracking failures. Therefore, this study proposes a vision improvement algorithm for mobile robots in view of multi feature fusion; This algorithm introduces line surface features and Manhattan Frame on the basis of traditional algorithms, and proposes an improved algorithm in view of multi-sensor fusion to improve tracking accuracy. The experiment shows that the average Root-mean-square deviation of the position of the improved mobile robot vision algorithm in view of multi feature fusion is 0.02 in nine data packets of the Tum dataset; The average Root-mean-square deviation of the position of the data packet successfully tracked by the traditional wheeled robot vision algorithm is 0.016; It improved the average accuracy by 11.11%, which is 31.03% higher than the average accuracy of the Manhattan wheeled robot vision algorithm. Compared to the multi feature fusion based vision improvement algorithm for mobile robots and the closed-loop detection based multi-sensor improvement algorithm, the accuracy of the closed-loop detection based multi-sensor improvement algorithm has increased by 0.655% and 10.47%, respectively. The outcomes indicate that the improved algorithm can improve the accuracy of mobile robot tracking, thereby expanding its application range. |
| first_indexed | 2024-03-11T22:33:46Z |
| format | Article |
| id | doaj.art-56a9b3a76c234677aa10559b634412a3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T22:33:46Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-56a9b3a76c234677aa10559b634412a32023-09-22T23:00:58ZengIEEEIEEE Access2169-35362023-01-011110065910067110.1109/ACCESS.2023.331532610251515Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature FusionXiaomei Hu0https://orcid.org/0009-0003-7937-4186Luying Zhu1Ping Wang2Haili Yang3Xuan Li4College of Intelligent Science and Engineering, Yantai Nanshan University, Yantai, ChinaCollege of Intelligent Science and Engineering, Yantai Nanshan University, Yantai, ChinaCollege of Intelligent Science and Engineering, Yantai Nanshan University, Yantai, ChinaSchool of Electric, Civil Engineering and Architecture, Shanxi University, Taiyuan, ChinaCollege of Intelligent Science and Engineering, Yantai Nanshan University, Yantai, ChinaTraditional wheeled robot vision algorithms suffer from low texture tracking failures. Therefore, this study proposes a vision improvement algorithm for mobile robots in view of multi feature fusion; This algorithm introduces line surface features and Manhattan Frame on the basis of traditional algorithms, and proposes an improved algorithm in view of multi-sensor fusion to improve tracking accuracy. The experiment shows that the average Root-mean-square deviation of the position of the improved mobile robot vision algorithm in view of multi feature fusion is 0.02 in nine data packets of the Tum dataset; The average Root-mean-square deviation of the position of the data packet successfully tracked by the traditional wheeled robot vision algorithm is 0.016; It improved the average accuracy by 11.11%, which is 31.03% higher than the average accuracy of the Manhattan wheeled robot vision algorithm. Compared to the multi feature fusion based vision improvement algorithm for mobile robots and the closed-loop detection based multi-sensor improvement algorithm, the accuracy of the closed-loop detection based multi-sensor improvement algorithm has increased by 0.655% and 10.47%, respectively. The outcomes indicate that the improved algorithm can improve the accuracy of mobile robot tracking, thereby expanding its application range.https://ieeexplore.ieee.org/document/10251515/Multi feature fusionmobile robotsvisual algorithmsmulti sensor fusionencoder |
| spellingShingle | Xiaomei Hu Luying Zhu Ping Wang Haili Yang Xuan Li Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature Fusion IEEE Access Multi feature fusion mobile robots visual algorithms multi sensor fusion encoder |
| title | Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature Fusion |
| title_full | Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature Fusion |
| title_fullStr | Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature Fusion |
| title_full_unstemmed | Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature Fusion |
| title_short | Improved ORB-SLAM2 Mobile Robot Vision Algorithm Based on Multiple Feature Fusion |
| title_sort | improved orb slam2 mobile robot vision algorithm based on multiple feature fusion |
| topic | Multi feature fusion mobile robots visual algorithms multi sensor fusion encoder |
| url | https://ieeexplore.ieee.org/document/10251515/ |
| work_keys_str_mv | AT xiaomeihu improvedorbslam2mobilerobotvisionalgorithmbasedonmultiplefeaturefusion AT luyingzhu improvedorbslam2mobilerobotvisionalgorithmbasedonmultiplefeaturefusion AT pingwang improvedorbslam2mobilerobotvisionalgorithmbasedonmultiplefeaturefusion AT hailiyang improvedorbslam2mobilerobotvisionalgorithmbasedonmultiplefeaturefusion AT xuanli improvedorbslam2mobilerobotvisionalgorithmbasedonmultiplefeaturefusion |