Over the past few years, LiDAR data has become one of the most reliable foundations for creating precise and accurate 3D models. Industries like mining, construction, and infrastructure are leveraging these models for routine inspections, safety assessments, tracking asset changes over time, and supporting project planning.
The outputs professionals get from 3D models made with LiDAR data include detailed digital twins, accurate 2D and 3D measurements, the ability to locate defects within assets, exporting data in common formats like *.e57, *.las, *.laz, and *.ply, and merging multiple georeferenced models to track asset changes over time.
Regardless of the industry or output, model quality is crucial. If the data isn't accurate—defined in 3D modeling—then 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 differences between models processed using FlyAware and FARO Connect.
### About the Elios 3's LiDAR Payload
The Elios 3 comes with Ouster’s OS0-128 Rev 7 LiDAR sensor and SLAM capabilities, allowing real-time 3D model creation during flights. After the flight, users can process the collected LiDAR data with FARO Connect to create precise 3D models.
The 3D Live Model and post-processed model have distinct uses. The 3D Live Model is used during missions for navigation, route planning, and verifying scan coverage, while the post-processed model provides an accurate point cloud.
### Defining Terms: Global Accuracy, Georeferenced Accuracy, and Drift in 3D Mapping
Global accuracy relates 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 like target-based registration and ICP.
Drift refers to the cumulative decrease in accuracy over time during data capture. For example, error on a 30m measurement is likely smaller than on a 300m measurement due to accumulated errors as the scanner moves.
### Global Accuracy and Georeferenced Accuracy Assessments with the Elios 3
To compare the global and georeferenced accuracy of the Elios 3's point clouds, identical captures were processed using both FlyAware and FARO Connect. Experts from FARO and Flyability conducted tests in an industrial factory.
#### Establishing a Control
For accuracy assessment, a more accurate system like a Terrestrial Laser Scanner (TLS) was used as a benchmark. The TLS provided a control dataset, which took over six hours to acquire, compared to 8.5 minutes for the Elios 3.
#### Test Environment
The test was conducted in the Blue Factory in Fribourg, Switzerland, featuring 12 rooms separated by doorways, providing a representative industrial environment for the Elios 3.
#### Collecting the Data
Three scans were performed with the Elios 3, following the same flight path. All scans started and ended in the same location, ensuring consistency. The average flight time was 8 minutes and 30 seconds over a ~450 meters path.
#### Data Processing & Centroid Extraction
Standard processing parameters were used, with no reprocessing or filtering. An extraction tool identified targets in both the Elios 3 and TLS data, comparing centroids for accuracy.
### Assessing Global Accuracy—Distance Measurements
Distance measurements were taken between pairs of centroids in both the control TLS data and the Elios 3 scans. Residuals were calculated, followed by RMSE and mean error calculations.
The results showed that processing with FARO Connect produced a global accuracy RMSE of 3.5 cm, compared to 18.3 cm with FlyAware. This highlights the impact of drift on longer-range measurements.
### Assessing Georeferenced Accuracy—Cloud-to-Cloud Alignment around Take-Off
Georeferenced accuracy assesses both global accuracy and the accuracy of the georeferencing technique. A 15-meter section around the take-off location was used for cloud-to-cloud registration, simulating a real-world use case.
The results showed that processing with FARO Connect produced an accuracy RMSE of 11.0 cm with a drift of 0.19%, compared to 64.9 cm with a drift of 1.41% using FlyAware. This indicates significant improvements in accuracy and reduced drift with FARO Connect.
### Conclusion
The test results show that the Elios 3’s point clouds processed with FARO Connect produce high accuracy and lower drift compared to those processed with FlyAware. On average, processing with FARO Connect improved global accuracy by 5.2 times.
While the Live Model offers real-time visualization, its accuracy of 182 mm makes it unsuitable for critical applications. The Cloud-to-Cloud assessment shows how the Elios 3 can map and georeference inaccessible environments effectively.
Looking at georeferenced accuracies, point clouds processed with FARO Connect were 5.9 times more accurate than those processed with FlyAware, primarily due to higher system drift in FlyAware processing.
These results clearly demonstrate the advantages of using FARO Connect for enhanced accuracy and reliability in LiDAR data processing.
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