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With the development of digital transmission and transformation systems, digital substations have become a prevalent trend in the electric power industry. However, the presence of many electrical components in substation environments with varying textures poses challenges for the reconstruction of a digital three-dimensional model and robot operation. To deal with that problem, this paper introduces the rich line features present in substation environments into the frontend feature tracking of 3D reconstruction in order to improve the accuracy of the 3D reconstruction. The optimized LSD algorithm is utilized for extracting line features in weak texture scenes. Additionally, a line segment merging algorithm is proposed to reduce the rate of mismatched line features and avoid redundancy through the combination of similar or repeated line segments. Finally, the paper presents a novel line feature matching method based on geometric constraints during the stage of online feature matching, which has the ability to improve matching accuracy while decreasing matching time. The experimental results demonstrate that the front-end visual odometer's optimization method proposed in this paper can offer better feature extraction and matching initial values compared to traditional methods for nonlinear optimization of the back-end of 3D reconstruction and loopback detection.
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