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• W4386066051 abstract "At present, the research of visual Simultaneous Localization and Mapping (SLAM) technology focuses on its robustness in complex environments. Since most of the manually extracted local features represented by ORBs do not perform satisfactorily in complex environments, researchers are focusing on the utilization of Convolutional Neural Networks (CNN) to extract the feature points. In other words, the end-to-end feature point extraction methods have become a hot research topic recently. In addition, one of the typical problems is the low-texture characteristic for the visual navigation in the complex environments. In this condition, the end-to-end feature point extraction methods are still insufficient in feature point extraction. Fortunately, the geometric elements, such as some line-based geometric primitives, are adopted to enhance the performance of feature extraction. This paper proposed the DPL-SLAM, a monocular vision SLAM system by integrating the CNN point features and line features. The experimental tests with TUM RGB-D dataset have shown that higher localization accuracy could be achieved in low-texture environments with geometrical features and high degree of robustness, especially when the proposed DPL-SLAM algorithm compared with the conventional ORB-SLAM2 algorithm." @default.
• W4386066051 created "2023-08-23" @default.
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• W4386066051 date "2023-08-06" @default.
• W4386066051 modified "2023-09-23" @default.
• W4386066051 title "DPL-SLAM: A SLAM System by Deep Learning Point Features and Line Features Integration" @default.
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• W4386066051 doi "https://doi.org/10.1109/icma57826.2023.10216196" @default.
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