Abstract: The front-end feature matching module of traditional SLAM systems is characterized by sparse or dense feature points, it is difficult to generate accurate camera trajectory and scene reconstruction results, in response to this problem, the author studied a fast reconstruction algorithm for any path based on V-SLAM, by using improved feature matching algorithms to accurately match feature points, the accuracy of scene sparse reconstruction and camera trajectory recovery has been improved, the backend optimization thread adopts segmented optimization matching to reduce the computational burden of reconstruction, and the performance of the V-SLAM system was improved through parallel processing, the matching results and camera trajectory error comparison results showed that the improved V-SLAM algorithm can quickly recover camera trajectory and scene reconstruction, with the development of multi-sensor collaborative coupling and multi view fusion technology, the V-SLAM method proposed by the author can add virtual 3D objects to real scenes, and the V-SLAM system can extract feature points in the screen in real-time and detect planar objects in the scene, ensure that multiple virtual objects in the scene meet geometric consistency with the actual scene, in the experiment, two objects were added to the virtual scene, users can interactively scale objects and add them without being affected by camera movements, ensuring consistency between objects and the real scene.

Wei Cui, Yuefan Zhao and Litao Sun, “Visual Recognition and Localization of Industrial Robots Based on SLAM Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01511135

@article{Cui2024,
title = {Visual Recognition and Localization of Industrial Robots Based on SLAM Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01511135},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01511135},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Wei Cui and Yuefan Zhao and Litao Sun}
}