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How Accurate Are The 3D Models You Can Make With FlyAware?
Over the past few years, LiDAR data has quickly become one of the most reliable foundations for creating precise and accurate 3D models. Industries such as 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 obtain 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 to common 3D point cloud formats like *.e57, *.las, *.laz, and *.ply, and merging multiple georeferenced 3D models to track changes in assets over time.
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 differences between models processed using FlyAware and FARO Connect.
About the Elios 3's LiDAR Payload
Flyability’s 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 flight. After a flight, users can process the collected LiDAR data with FARO Connect to generate precise, accurate 3D models. The 3D Live Model serves navigation, route planning, and coverage verification during missions, while the post-processed model from FARO Connect provides an accurate point cloud.
Defining Our 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 caused by alignment methods like target-based registration and ICP. Drift refers to the cumulative decrease in accuracy over the duration of a capture, typically increasing with the size of the asset or the distance measured.
For example, the error on a 30m measurement is likely smaller than on a 300m measurement due to accumulated errors from system drift. A 1% drift on a 300m measurement corresponds to a 3m error compared to reality.
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 within an industrial factory.
Establishing a Control
When assessing the accuracy of any system, a second measurement system must serve as the benchmark. For mobile mapping solutions like the Elios 3, a Total Station (TPS) or Terrestrial Laser Scanner (TLS) is used as a control, offering higher accuracy.
Test Environment
The tests were conducted in the Blue Factory in Fribourg, Switzerland, consisting of 12 rooms of varying sizes, providing a representative industrial environment suitable for the Elios 3.
Collecting the Data
Three scans were carried out with the Elios 3 following the same approximate flight path to ensure consistency. All scans started and ended in the same location, maintaining best practices for SLAM data capture. The average flight time was 8 minutes and 30 seconds over a ~450 meters flight path. Datasets processed using FARO Connect averaged 108 million points per scan, while those processed using FlyAware averaged 21 million points per scan.
Data Processing & Centroid Extraction
To ensure the test was representative, standard Flyability processing parameters were used in FARO Connect. No reprocessing, filtering, or decimation was performed. The FlyAware Live Model was processed onboard the Elios 3 without reprocessing, filtering, or decimation.
Assessing Global Accuracy—Distance Measurements
To evaluate the global accuracy of the Elios 3, distance measurements were taken and compared against TLS control data. Steps included measuring distances between centroids, finding residuals, calculating RMSE, and determining the mean error.
Results
Processing the Elios 3 data using FlyAware yielded a global accuracy RMSE of 18.3 cm (7.20 inches), while processing with FARO Connect produced a global accuracy RMSE of 3.5 cm (1.38 inches). FARO Connect provided results that were 14.8 cm more accurate on average, with higher RMSE from FlyAware datasets attributed to longer ranged point-point measurements and system drift.
Assessing Georeferenced Accuracy—Cloud-to-Cloud Alignment around Take-Off
In this section, we assess the first registration method—cloud-to-cloud alignment around the take-off location. Georeferenced accuracy evaluates the global accuracy of the system and the accuracy of the georeferencing technique used.
To simulate a common use case for Elios 3 scans, the Elios 3 point cloud was aligned to the reference model around the take-off location. Only having control in one section of the scan environment causes inaccuracies to propagate throughout the scan.
Results
The georeferenced accuracy assessment of the Elios 3 data when processed using FlyAware yielded an accuracy RMSE value of 64.9 cm (25.6 inches) with a drift of 1.41%. Processing datasets using FARO Connect produced an accuracy RMSE of 11.0 cm (4.35 inches) with a drift of 0.19%. FARO Connect provided results that were on average 53.9 cm more accurate, with FlyAware datasets experiencing a 1.22% increase in drift.
Conclusion
The test results show that the Elios 3’s point clouds processed with FARO Connect produce high accuracy and lower drift compared with those processed using FlyAware. On average, processing using FARO Connect improved global accuracy by 5.2 times compared to processing using FlyAware alone. Although the Live Model provides real-time visualization, its average accuracy of 182 mm (7.2 inches) makes it unsuitable for certain applications.
The Cloud-to-Cloud assessment demonstrates how the Elios 3 can be implemented to map and georeference inaccessible environments. Looking at the georeferenced accuracies, point clouds processed using FARO Connect were 5.9 times more accurate than those processed using Flyaware. This can be attributed to the higher system drift accumulated during FlyAware processing, which was 7.42 times the value produced by FARO Connect.
These results clearly illustrate the difference in accuracy between the two processing methods, especially at distances over 75 meters from the take-off location.