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The focus of this article is on the precision and local accuracy of 3D models generated using data from Flyability's Elios 3 drone, processed with GeoSLAM Connect. For insights into global and georeferenced accuracy in a large warehouse setting, check out our related article.
Please note that the Introduction and Why We Performed These Tests sections are identical to those in the linked article. If you’ve already read that piece, we recommend starting at the Defining Our Terms section below.
### Overview of Test Results
To evaluate the performance of the Elios 3 with GeoSLAM Connect, our team of GeoSLAM experts and Flyability product specialists conducted comprehensive tests:
- **What Was Tested**: System precision and local accuracy of 3D models derived from LiDAR data collected by the Elios 3.
- **Who Conducted the Testing**: GeoSLAM 3D mapping specialists and members of the Flyability product team.
- **Testing Methods**: Local accuracy was assessed using a Plane-to-Plane analysis, while system precision was evaluated via Range Noise analysis.
- **Reference Model**: A TLS (Terrestrial Laser Scanning) model created with a Riegl VZ-400 was used as the benchmark, processed with RiScan Pro V2.14.1.
- **Results—Local Accuracy**: All comparisons fell within ±16mm (.63 inches), with a Mean Absolute Normal Distance of 8mm (.32 inches).
- **Results—Precision Analysis**: The standard deviation of all planes was within 15mm (.59 inches), with a Mean Standard Deviation of 8mm (.32 inches).
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### Introduction
In recent years, LiDAR data has emerged as one of the most dependable resources for creating precise and accurate 3D models. Industries such as mining, construction, and infrastructure leverage these models for regular inspections, safety assessments, tracking asset changes over time, and supporting project planning. Outputs from these models include detailed digital twins, precise 2D and 3D measurements, defect localization, export capabilities for common 3D point cloud formats (like *.e57, *.las, *.laz, and *.ply), and the ability to merge multiple georeferenced models for longitudinal asset monitoring.
The quality of a 3D model is paramount to its utility. If the data lacks precision and accuracy—terms with specific meanings in 3D modeling, explained later—it might fail to accurately represent the real world and thus offer limited actionable insights. This article details findings from tests performed by GeoSLAM and Flyability teams to evaluate the precision and local accuracy of 3D models created with the Elios 3 and GeoSLAM Connect, comparing them to established leaders in mobile mapping solutions like the ZEB Revo and ZEB Horizon.
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### Why We Conducted These Tests
Flyability’s Elios 3 integrates Ouster’s OS0-32 LiDAR sensor and SLAM (Simultaneous Localization and Mapping) functionality, enabling real-time 3D model creation during flight. Post-flight, users can process the collected LiDAR data with GeoSLAM Connect to generate precise, accurate 3D models.
While the 3D Live Model serves immediate purposes like navigation and route planning during missions, the post-processed model provides a highly accurate point cloud. By combining Flyability’s field-proven collision-resistant technology with GeoSLAM Connect’s mapping capabilities, inaccessible spaces can now be mapped effectively.
However, potential Elios 3 users might wonder about the implications of mounting a LiDAR payload on an indoor drone and processing the data afterward. Questions arise such as:
- Will the drone’s vibrations or environmental factors like dust or moisture affect the precision of the resulting models?
- How accurate and useful will the final 3D models be?
To answer these questions, we thoroughly analyzed the system precision and local accuracy of the Elios 3’s point clouds after processing with GeoSLAM Connect. Our approach ensured both representativeness and repeatability in the testing procedures.
Read on to discover the outcomes.
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### Defining Our Terms: Precision and Local Accuracy in 3D Mapping
In the context of 3D models generated from LiDAR data, the terms "precision" and "accuracy" have specific meanings.
**Accuracy** refers to the degree of conformity of a measured quantity to its actual (or benchmark) value. For instance, if a measured distance of 100mm (3.9 inches) in your point cloud corresponds to an actual distance of 500mm (19.7 inches), the measurement lacks accuracy.
Accuracy is critical for ensuring a 3D model reflects reality, including correctly representing corner shapes and preventing overlapping walls.
**Local accuracy**, on the other hand, pertains to the distance between two points in a point cloud when viewed from a single position (such as measuring a single room). This article focuses exclusively on local accuracy testing with the Elios 3 and GeoSLAM Connect.
**Precision**, defined as the degree to which repeated measurements yield consistent results, is often referred to as "noise" or "repeatability." The required level of precision depends on the intended application. For example, precision in the range of a few centimeters suffices for stockpile measurements in mining, whereas construction may demand even finer precision.
It’s worth noting that precision sets a lower limit for accuracy, particularly when features are picked manually from the point cloud. Lower precision leads to higher noise, making it more challenging to select the exact feature point.
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### System Precision and Local Accuracy Assessments with the Elios 3
To evaluate the precision and local accuracy of the Elios 3 combined with GeoSLAM Connect, our team executed:
- A Plane-to-Plane analysis to assess local accuracy.
- A Range Noise evaluation to determine system precision.
#### Establishing a Control
When evaluating the accuracy of any system, a second, more accurate measurement system must serve as the benchmark. Industry standards favor using a Total Station (TPS) or Terrestrial Laser Scanner (TLS) for this purpose, as their accuracy surpasses that of mobile mapping solutions like the Elios 3.
The reason TPS and TLS achieve greater accuracy is their ability to capture data from a single stationary position, with multiple positions aligned via point-matching algorithms. In contrast, mobile mapping solutions like the Elios 3 collect data continuously as the device moves through the environment.
#### Collecting the Data
Our team gathered data from an indoor planar surface environment using both the Elios 3 and an industry-standard TLS. The Riegl VZ-400 TLS served as the control for these tests, defining the accuracy from a single position at a specified confidence level. The Riegl VZ-400 boasts an accuracy of 5mm (.2 inches) at 1-sigma, meaning 68% of all measurements lie within this range. From the Riegl point cloud, the reference model was created to act as the ground truth.
#### Aligning the Elios 3 Point Cloud to the Reference Model
To compare the Elios 3 point cloud with the TLS reference model effectively, the Elios 3 model was first aligned to the reference model. This alignment adjusted the position and orientation of the point cloud data to match the coordinate system of the TLS reference model.
GeoSLAM used PolyWorks|Inspector MRS2019 IR3 software to align the Elios 3 point cloud with the TLS reference model. Here are the steps taken:
1. **Manual Alignment**: A rough alignment was performed manually in PolyWorks to provide an initial approximation.
2. **Computed Transformation Matrix**: The Best-Fit Automatic Alignment function was used to calculate the transformation matrix.
3. **Apply Transformation**: The computed transformation matrix was applied to align the Elios 3 data with the reference model.
#### Assessing Local Accuracy—Plane-to-Plane Comparison
A Plane-to-Plane analysis was conducted by fitting planes to both the Elios 3 data and the reference model. The Normal Distance between the planes was then evaluated. Normal Distance was calculated using an automated workflow in PolyWorks MRS2019 IR3.
#### Assessing System Precision—Range Noise Analysis
To evaluate the precision of the Elios 3, Range Noise Analysis was performed. Range Noise represents the difference between each range reading (point) and the mean range value within the selected area. Areas chosen for this analysis included the planar surfaces used in the Plane-to-Plane comparison. Range Noise is expressed as the Standard Deviation from the mean point of the plane, with results reported at 1-sigma.
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### Test Environment
The accuracy and precision of the Elios 3’s point clouds were evaluated in a standard office environment featuring six planar surfaces, each approximately 1 meter (3.3 feet) square, positioned at regular intervals around the scan. Locations of the planar surfaces are illustrated in Figures 2 and 3.
[Insert Figure 2 and Figure 3 here]
Laser scanning reference spheres (145mm/5.7 inches diameter) were strategically placed throughout the environment to facilitate the registration of Terrestrial Laser Scans, creating the reference model. The reference model was generated using the LAZ output from RiSCAN Pro.
[Insert Figure 4 here]
Following the creation of the reference model, a drone pilot flew the Elios 3 along a recommended mapping flight path, beginning and ending in the same location. The flight trajectory is depicted in Figure 5.
[Insert Figure 5 here]
Post-flight, the LiDAR data collected by the Elios 3 was processed using GeoSLAM Connect v2.1.0, filtered to remove outliers, and exported in the LAZ file format.
[Insert Figure 6 here]
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### Test Results
#### Local Accuracy Assessment
Experts assessed the local accuracy of the Elios 3’s LiDAR data using Plane-to-Plane analysis. The Normal Distances between the planes in the reference model and the planes from the Elios 3 data processed with GeoSLAM Connect are summarized in Table 1.
The results indicate that all comparisons fell within ±16mm (.63 inches), with a Mean Absolute Normal Distance of 8mm (.32 inches).
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#### System Precision Assessment
The Range Noise Analysis results for the Elios 3 are presented in Table 2. The standard deviation of all planes was found to fall within 15mm (.59 inches), with a Mean Standard Deviation of 8mm (.32 inches) to 1-sigma.
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### Conclusion
Accuracy tests were conducted in a standard office environment featuring planar surfaces, using the mobile mapping system Elios 3 with GeoSLAM Connect. The data was compared against a reference model created with an industry-standard TLS, the Riegl VZ-400. Alignment and accuracy computations were performed using PolyWorks MRS2019 IR3.
Plane-to-Plane analysis revealed a mean Normal Distance of 8mm (.32 inches) between the reference model and the Elios 3/GeoSLAM Connect model. Range Noise analysis showed a mean Standard Deviation of 8mm (.32 inches) to 1-sigma, indicating strong performance comparable to traditional TLS systems and market-leading mobile mapping solutions like the ZEB Revo and ZEB Horizon.
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