Design of high-precision positioning augmented reality for electricity pipeline inspection based on ORB-SLAM3
In the daily inspection process of urban cable pipelines, due to the high concealment of urban underground cable networks, the use of traditional underground cable network GIS information not only has certain map recognition thresholds, but also cannot determine the specific actual location of underground cable networks in GIS information due to environmental changes, geological changes, vegetation changes, and other environmental reference changes in real-life scenarios. At the same time, due to the lack of spatial sense in viewing pipeline GIS information, the quality of line patrol inspection and the accuracy of pipeline excavation cannot be guaranteed. This article relies on GIS high-precision positioning technology, intelligent north pointing calibration, augmented reality technology based on ORB-SLAM3, data analysis and calculation, and multi-spatial data conversion visualization display technology to create cable network data platform, augmented reality visualization mobile terminal and other systems. This article mainly demonstrates that by combining GIS data of cable pipelines with high-precision positioning of mobile terminals, and transforming GIS two-dimensional spatial data of electricity pipeline into intuitive and visualized augmented reality SAR data, through ORBSLAM3, it provides a theoretical method for more stable and reliable real-space binding for SAR, laying the foundation for reducing the maintenance difficulty of urban cable pipeline data, improving the efficiency of cross-department business collaboration within power enterprises, and providing intensive data application capability support for urban management.
Authors
- Wei Zhou (ORCID: https://orcid.org/0000-0002-1570-7055)
Institutions
- Shanghai Electric (China) (CN)
Publication Details
- Published
- 2024-09-27
- DOI
- https://doi.org/10.1117/12.3037733
- Primary Topic
- Robotics and Sensor-Based Localization
- Type
- article
- Field-Weighted Citation Impact
- 0.00