Cognite works with customers to use robots, drones, and ROVs to efficiently capture inspection data. Data from robot sensors (video, audio, LiDAR data, gas detection, thermal images, etc.) is captured and stored in Cognite Data Fusion. Automate data capture by letting robots perform regular data collection missions. Increased frequency of data capture contributes to reduced operating risk.
Increased volume and quality of visual data enables the use of computer vision. Deep learning models are trained to detect maintenance needs, including surface properties such as rust and cracks or lighting fixtures that have stopped working. Comparison of data over time can help identify developing issues and misplaced objects.
Cognite takes the raw 3D and video data and performs optimization and processing to enable advanced visualizations. 3D CAD models are analyzed and transformed for efficient and lossless rendering of complex assets. Image, video, and LiDAR data can be processed to generate 3D-textured photogrammetry models. The 3D data is contextualized to link all elements to relevant assets, geographic location, and time.
All asset data can be combined into a complete 3D asset and field visualization, incorporating 3D CAD models, 3D terrain models, scanned point cloud data, photos and videos, 360-degree photos, and 3D photogrammetry models. The digital representation of the asset can then be overlayed with real-time sensor and event data, as well as provide access to all relevant documentation.
Optimized workflows allow users to efficiently interact with visual inspection data, recording relevant findings and conclusions. Combine visual analysis with numerical analysis to validate decisions. Integration towards relevant back-end systems allow for end-to-end completion of inspection workflows.