Over the past few years, LiDAR data has become one of the most reliable foundations for creating precise and accurate 3D models. Industries such as mining, construction, and infrastructure are using these models to conduct routine inspections, assess safety, track changes in assets over time, and support project planning. The outputs from 3D models created with LiDAR data include detailed digital twins, accurate 2D and 3D measurements, the ability to identify defects within assets, exporting data to common 3D point cloud formats like *.e57, *.las, *.laz, and *.ply, and merging multiple georeferenced 3D models to monitor asset changes.
Regardless of the industry or output, the quality of the model is crucial. If the data isn't accurate—defined in 3D modeling—it may not represent the real world well enough to offer valuable insights. This article presents findings from tests conducted by experts at FARO (formerly GeoSLAM) and the Flyability product team, highlighting the differences between models processed using FlyAware and FARO Connect.
The Elios 3 comes equipped with Ouster’s OS0-128 Rev 7 LiDAR sensor and SLAM capabilities, allowing it to create 3D models in real-time during flights. Users can process the collected LiDAR data with FARO Connect to generate precise 3D models. The 3D Live Model and post-processed model serve different purposes: the former aids in navigation and route planning, while the latter provides an accurate point cloud.
Global accuracy refers to the distance between two points in a point cloud where the object cannot be viewed from a single position. Georeferenced accuracy includes global accuracy plus inaccuracies from alignment methods. Drift is the cumulative decrease in accuracy over time, often due to system errors accumulating as the scanner moves through an environment.
To evaluate the Elios 3's global and georeferenced accuracy, identical captures were processed using both FlyAware and FARO Connect. A Terrestrial Laser Scanner (TLS) served as the control, providing a benchmark for accuracy. The test was conducted in an industrial factory setting, with 15x retroreflective targets placed around the environment.
Three scans were performed with the Elios 3, following a consistent flight path. The data was processed using standard parameters found in FARO Connect, without reprocessing, filtering, or decimation. An extraction tool was used to identify centroids in both the Elios 3 and TLS data, comparing them to assess accuracy.
The results showed that processing with FARO Connect yielded a global accuracy RMSE of 3.5 cm (1.38 inches), compared to 18.3 cm (7.20 inches) with FlyAware. FARO Connect improved global accuracy by 5.2 times. For georeferenced accuracy, FARO Connect provided an RMSE of 11.0 cm (4.35 inches) with a drift of 0.19%, whereas FlyAware had an RMSE of 64.9 cm (25.6 inches) and a drift of 1.41%. These results highlight the significant difference in accuracy and reliability between the two processing methods.
In conclusion, the Elios 3's point clouds processed with FARO Connect demonstrate higher accuracy and lower drift compared to those processed with FlyAware. While the Live Model offers real-time visualization, its average accuracy of 182 mm (7.2 inches) makes it unsuitable for critical applications. The Cloud-to-Cloud assessment shows the Elios 3's capability to map and georeference inaccessible environments, with FARO Connect significantly outperforming FlyAware in accuracy and drift management.
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