RGB-D SLAM Based on Extended Bundle Adjustment with 2D and 3D Information
AbstractIn the study of SLAM problem using an RGB-D camera, depth information and visual information as two types of primary measurement data are rarely tightly coupled during refinement of camera pose estimation. In this paper, a new method of RGB-D camera SLAM is proposed based on extended bundle adjustment with integrated 2D and 3D information on the basis of a new projection model. First, the geometric relationship between the image plane coordinates and the depth values is constructed through RGB-D camera calibration. Then, 2D and 3D feature points are automatically extracted and matched between consecutive frames to build a continuous image network. Finally, extended bundle adjustment based on the new projection model, which takes both image and depth measurements into consideration, is applied to the image network for high-precision pose estimation. Field experiments show that the proposed method has a notably better performance than the traditional method, and the experimental results demonstrate the effectiveness of the proposed method in improving localization accuracy. View Full-Text
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Di, K.; Zhao, Q.; Wan, W.; Wang, Y.; Gao, Y. RGB-D SLAM Based on Extended Bundle Adjustment with 2D and 3D Information. Sensors 2016, 16, 1285.
Di K, Zhao Q, Wan W, Wang Y, Gao Y. RGB-D SLAM Based on Extended Bundle Adjustment with 2D and 3D Information. Sensors. 2016; 16(8):1285.Chicago/Turabian Style
Di, Kaichang; Zhao, Qiang; Wan, Wenhui; Wang, Yexin; Gao, Yunjun. 2016. "RGB-D SLAM Based on Extended Bundle Adjustment with 2D and 3D Information." Sensors 16, no. 8: 1285.
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