Improved Point-Line Feature Based Visual SLAM Method for Complex Environments
Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more envir...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/13/4604 |
| _version_ | 1797527854188068864 |
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| author | Fei Zhou Limin Zhang Chaolong Deng Xinyue Fan |
| author_facet | Fei Zhou Limin Zhang Chaolong Deng Xinyue Fan |
| author_sort | Fei Zhou |
| collection | DOAJ |
| description | Traditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM. |
| first_indexed | 2024-03-10T09:49:43Z |
| format | Article |
| id | doaj.art-49b6d7430d8e4624bd13f7bf4cdf9d02 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T09:49:43Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-49b6d7430d8e4624bd13f7bf4cdf9d022023-11-22T02:51:56ZengMDPI AGSensors1424-82202021-07-012113460410.3390/s21134604Improved Point-Line Feature Based Visual SLAM Method for Complex EnvironmentsFei Zhou0Limin Zhang1Chaolong Deng2Xinyue Fan3College of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaCollege of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaCollege of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaCollege of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, ChinaTraditional visual simultaneous localization and mapping (SLAM) systems rely on point features to estimate camera trajectories. However, feature-based systems are usually not robust in complex environments such as weak textures or obvious brightness changes. To solve this problem, we used more environmental structure information by introducing line segments features and designed a monocular visual SLAM system. This system combines points and line segments to effectively make up for the shortcomings of traditional positioning based only on point features. First, ORB algorithm based on local adaptive threshold was proposed. Subsequently, we not only optimized the extracted line features, but also added a screening step before the traditional descriptor matching to combine the point features matching results with the line features matching. Finally, the weighting idea was introduced. When constructing the optimized cost function, we allocated weights reasonably according to the richness and dispersion of features. Our evaluation on publicly available datasets demonstrated that the improved point-line feature method is competitive with the state-of-the-art methods. In addition, the trajectory graph significantly reduced drift and loss, which proves that our system increases the robustness of SLAM.https://www.mdpi.com/1424-8220/21/13/4604visual SLAMpoint and line featureadaptive ORBdata associationLSD feature extractionreprojection error |
| spellingShingle | Fei Zhou Limin Zhang Chaolong Deng Xinyue Fan Improved Point-Line Feature Based Visual SLAM Method for Complex Environments Sensors visual SLAM point and line feature adaptive ORB data association LSD feature extraction reprojection error |
| title | Improved Point-Line Feature Based Visual SLAM Method for Complex Environments |
| title_full | Improved Point-Line Feature Based Visual SLAM Method for Complex Environments |
| title_fullStr | Improved Point-Line Feature Based Visual SLAM Method for Complex Environments |
| title_full_unstemmed | Improved Point-Line Feature Based Visual SLAM Method for Complex Environments |
| title_short | Improved Point-Line Feature Based Visual SLAM Method for Complex Environments |
| title_sort | improved point line feature based visual slam method for complex environments |
| topic | visual SLAM point and line feature adaptive ORB data association LSD feature extraction reprojection error |
| url | https://www.mdpi.com/1424-8220/21/13/4604 |
| work_keys_str_mv | AT feizhou improvedpointlinefeaturebasedvisualslammethodforcomplexenvironments AT liminzhang improvedpointlinefeaturebasedvisualslammethodforcomplexenvironments AT chaolongdeng improvedpointlinefeaturebasedvisualslammethodforcomplexenvironments AT xinyuefan improvedpointlinefeaturebasedvisualslammethodforcomplexenvironments |