Over the past few years, LiDAR data has rapidly become one of the most reliable foundations for creating precise and accurate 3D models. Industries like mining, construction, and infrastructure are leveraging these models to conduct routine inspections, assess safety, track changes in assets over time, and support project planning. The outputs professionals get from LiDAR-based 3D models include detailed digital twins, accurate 2D and 3D measurements, the ability to identify defects within assets, exporting data to common point cloud formats like *.e57, *.las, *.laz, and *.ply, and merging multiple georeferenced models to monitor asset changes.
The quality of a model is crucial to its usefulness. If the data isn't accurate—defined specifically in 3D modeling—then it might not represent the real world well enough to offer valuable insights. This article highlights findings from tests conducted by experts at FARO (formerly GeoSLAM) and the Flyability product team, comparing models processed using FlyAware and FARO Connect.
The Elios 3, equipped with Ouster’s OS0-128 Rev 7 LiDAR sensor and SLAM capabilities, can create 3D models in real-time during flight. After the mission, users process the collected LiDAR data using FARO Connect to generate precise 3D models. While the 3D Live Model is useful for navigation and route planning during a mission, the post-processed model offers more accurate point clouds.
Global accuracy refers to the distance between two points in a point cloud, while georeferenced accuracy includes alignment inaccuracies. Drift, the cumulative loss of accuracy over time, is influenced by factors like movement and lack of ground control points. For example, a 300m measurement could have an error of up to 3m due to drift.
To evaluate the global and georeferenced accuracy of the Elios 3, identical captures were processed using both FlyAware and FARO Connect. A Terrestrial Laser Scanner (TLS) served as the control, providing a benchmark. The test was conducted in the Blue Factory in Fribourg, Switzerland, with 15 retroreflective targets placed throughout the environment.
Three scans were performed, each following the same flight path. The Elios 3 captured data in about 8.5 minutes, while the TLS took over six hours. Data processing involved extracting centroids from the targets and comparing them against the TLS data. Results showed that FARO Connect produced significantly higher accuracy and lower drift compared to FlyAware.
In assessing georeferenced accuracy, the Elios 3 point cloud was aligned to the reference model around the take-off location. This simulated a real-world scenario where control points are limited. The results indicated that FARO Connect provided better alignment and reduced drift, making it more suitable for applications requiring high precision.
Overall, the tests demonstrated that the Elios 3, when processed with FARO Connect, delivers superior accuracy and reliability, meeting survey requirements effectively. While the Live Model is helpful for real-time navigation, its lower accuracy makes it unsuitable for critical applications. The Cloud-to-Cloud assessment highlighted the importance of proper georeferencing, especially in inaccessible environments where drift can significantly impact accuracy. These findings emphasize the value of advanced processing tools like FARO Connect in ensuring the highest quality 3D models.
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