Paper
6 May 2022 A positioning algorithm for fusion of image point and line features
Feng Pan, Jianxin Shen, Shun Qin, Xin Lin
Author Affiliations +
Proceedings Volume 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022); 1225617 (2022) https://doi.org/10.1117/12.2635689
Event: 2022 International Conference on Electronic Information Engineering, Big Data and Computer Technology, 2022, Sanya, China
Abstract
In order to solve the problem that the ORB-SLAM2 algorithm will cause positioning failure in a scene with a relatively single texture, the line features in the environment are extracted and matched. The point and line feature constraints are used to ensure the positioning accuracy. Firstly, the acquired image was processed by gradient density filtering. Then the feature points in the image were extracted by the ORB algorithm, the linear features in the image were extracted by the LSD algorithm and the linear features in the image were described by the LBD algorithm. In the above process, a certain strategy was used to select the keyframes. Next the point and line reprojection error term was constructed and the weighting strategy was used to weight the reprojection error. Finally, the nonlinear optimization method was used to obtain accurate pose information. The experimental results on the EuRoC dataset prove that the accuracy of the positioning algorithm fusing point and line features is higher than that of the positioning algorithm using only point features.
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Feng Pan, Jianxin Shen, Shun Qin, and Xin Lin "A positioning algorithm for fusion of image point and line features", Proc. SPIE 12256, International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2022), 1225617 (6 May 2022); https://doi.org/10.1117/12.2635689
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KEYWORDS
Feature extraction

Image filtering

Image processing

Cameras

Image fusion

Error analysis

Optical filters

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