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How Accurate Are The 3D Models You Can Make With FlyAware?
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 for routine inspections, safety assessments, tracking asset changes over time, and supporting project planning. The outputs professionals get from LiDAR-based 3D models include detailed digital twins, accurate 2D and 3D measurements, defect localization, exporting to common file formats like *.e57, *.las, *.laz, and *.ply, and merging multiple georeferenced models to track asset changes.
The quality of a model is crucial for its usefulness. If the data isn't accurate, it may not represent the real world well enough to provide valuable insights. This article covers findings from tests performed by experts at FARO (formerly GeoSLAM) and the Flyability product team comparing models processed using FlyAware and FARO Connect.
The Elios 3 comes with Ouster’s OS0-128 Rev 7 LiDAR sensor and SLAM capabilities, enabling real-time 3D model creation during flights. Users can process the collected LiDAR data with FARO Connect to generate precise 3D models. The 3D Live Model is useful for navigation and route planning, while the post-processed model offers an accurate point cloud.
Global accuracy refers to the distance between two points in a point cloud, while georeferenced accuracy adds inaccuracies from alignment methods. Drift is the cumulative decrease in accuracy over time, which can be significant in mobile mapping solutions. For example, a 1% drift on a 300m measurement results in a 3m error.
To assess 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 high-accuracy benchmark data. The test environment was a factory in Fribourg, Switzerland, with 15x retroreflective targets placed around the area.
Three scans were conducted, each following the same flight path. The average flight time was 8 minutes and 30 seconds over a ~450-meter path. Datasets processed with FARO Connect had 108 million points per scan, while those processed with FlyAware had 21 million points per scan. An extraction tool identified the 15 targets in both datasets, comparing centroids for accuracy.
For global accuracy assessment, distance measurements were taken between pairs of centroids. Residuals were calculated, followed by RMSE for each scan. The results showed that FARO Connect produced a global accuracy RMSE of 3.5 cm, compared to 18.3 cm for FlyAware. This highlights the impact of system drift on long-range measurements.
In the georeferenced accuracy assessment, the Elios 3 point cloud was aligned to the TLS reference around the take-off location. This simulated a scenario where only the starting point had control. The results showed that FARO Connect achieved an accuracy RMSE of 11.0 cm with a 0.19% drift, while FlyAware had an RMSE of 64.9 cm with a 1.41% drift.
These results emphasize the importance of processing software in achieving high accuracy and minimizing drift. While the Live Model provides real-time visualization, its lower accuracy makes it unsuitable for critical applications. The Cloud-to-Cloud assessment demonstrated the Elios 3's ability to map and georeference inaccessible areas, with FARO Connect significantly outperforming FlyAware in accuracy and drift management.
Overall, the test results highlight that the Elios 3, when processed with FARO Connect, delivers high accuracy and minimal drift, making it suitable for survey requirements. The visual comparisons further illustrate the differences between FlyAware and FARO Connect results, underscoring the benefits of using advanced processing tools for LiDAR data.