Note: The first two sections of this article (the Introduction and Why We Performed These Tests) are identical to the ones in the previous white paper. If you've already read that document, we recommend starting from the Defining Our Terms section below.

TEST RESULTS OVERVIEW

Introduction

In recent years, LiDAR data has become a cornerstone for creating accurate and reliable 3D models across various industries. From mining and construction to infrastructure management, these models are used for inspections, safety assessments, asset tracking, and project planning.

Professionals in these fields rely on 3D models generated from LiDAR data to create digital twins, take precise measurements, identify defects, export point cloud files, and merge multiple scans for long-term monitoring. The quality of the model is essential for its usefulness. If the data lacks precision and accuracy—terms with specific meanings in 3D modeling—it may not accurately represent real-world conditions.

This article presents findings from tests conducted by GeoSLAM and Flyability experts, comparing the system precision and local accuracy of 3D models created with the Elios 3 and GeoSLAM Connect against leading mobile mapping systems like the ZEB Revo and ZEB Horizon.

Why We Performed These Tests

Flyability’s Elios 3 features Ouster’s OS0-32 LiDAR sensor and SLAM technology, allowing it to generate 3D models in real time during flight. After landing, users can process the collected data with GeoSLAM Connect to produce highly accurate models.

The 3D Live Model serves for navigation and route planning during missions, while the post-processed model offers detailed point clouds. Processing with GeoSLAM Connect enhances Flyability’s collision-tolerant design, enabling effective mapping in confined spaces.

Potential users might wonder how environmental factors or vibrations affect the precision of the resulting models. To answer these questions, we conducted thorough evaluations of the Elios 3’s performance in terms of system precision and local accuracy when processed with GeoSLAM Connect.

Defining Our Terms: Precision and Local Accuracy in 3D Mapping

In 3D modeling, accuracy refers to how closely a measured value matches the actual value. For instance, if a 100mm measurement in your point cloud doesn’t match the true 500mm distance, it's not accurate.

Accuracy ensures that the model reflects reality correctly, including proper geometry and spatial relationships. Local accuracy relates to distances within a single view, such as the dimensions of a room. This article focuses only on local accuracy for the Elios 3 and GeoSLAM Connect.

Precision, on the other hand, measures how consistent repeated measurements are. If you measure the same points five times and get the same result each time, your model is precise. Precision is often referred to as noise or repeatability.

The required level of precision varies by industry. In mining, a few centimeters might be sufficient, while construction demands higher accuracy. Low precision increases noise, making it harder to extract exact features from the point cloud.

Accurate measurements fall in the bullseye. Precise measurements are tightly clustered.

System Precision and Local Accuracy Assessments with the Elios 3

To evaluate the precision and local accuracy of the Elios 3 with GeoSLAM Connect, GeoSLAM experts and Flyability engineers performed:

• A Plane-to-Plane analysis to assess local accuracy
• A Range Noise evaluation to test system precision

Establishing a Control

To measure the accuracy of a mobile mapping system like the Elios 3, a more accurate control system must be used. Industry standards typically use Total Stations (TPS) or Terrestrial Laser Scanners (TLS) for this purpose.

The Riegl VZ-400 TLS was used as the control in this study, offering an accuracy of 5mm at 1-sigma. This served as the ground truth for comparison with the Elios 3 data.

Collecting the Data

Data was captured from an indoor environment using both the Elios 3 and the Riegl VZ-400 TLS. The TLS data was processed into a reference model using RiScan Pro, which served as the benchmark for evaluating the Elios 3’s performance.

Aligning the Elios 3 Point Cloud to the Reference Model

The Elios 3 point cloud was aligned to the TLS reference model using PolyWorks|Inspector MRS2019 IR3. The alignment involved manual positioning followed by an automatic Best-Fit algorithm to minimize deviations.

Assessing Local Accuracy—Plane-to-Plane Comparison

A Plane-to-Plane analysis was conducted to compare the Elios 3 data with the reference model. Normal Distance, the difference between corresponding planes, was calculated to determine local accuracy.

Assessing System Precision—Range Noise Analysis

Range Noise, defined as the deviation of individual range readings from the mean, was used to evaluate system precision. The results showed that the standard deviation of the Elios 3 point cloud remained well within acceptable limits.

Test Environment

The test was conducted in a standard office with six planar surfaces, each approximately 1 meter square. These surfaces were strategically placed to ensure comprehensive coverage of the scanning area.

System Precision and Local Accuracy Test Results

The following results were obtained from the tests conducted on the Elios 3 with GeoSLAM Connect:

Assessing Local Accuracy

The Plane-to-Plane analysis revealed that the Elios 3 model compared favorably with the reference model. The Mean Absolute Normal Distance was 8mm (.32 inches), with all differences falling within +/- 16mm (.63 inches).

Table 1. Local plane-to-plane accuracy

Assessing System Precision

The Range Noise analysis confirmed that the Elios 3 delivers high precision. The mean standard deviation between the Elios 3/GeoSLAM Connect model and the reference model was 8mm (.32 inches) to 1-sigma, with all planes within 15mm (.59 inches).

Table 2. Range noise precision

Conclusion

Tests were conducted in a standard office environment with planar surfaces using the Elios 3 and GeoSLAM Connect. The data was compared to a reference model created with a Riegl VZ-400 TLS. The Elios 3 point cloud was processed with GeoSLAM Connect v2.1.0, and the reference data was processed with RiScan Pro 2.14.1. Alignment and accuracy calculations were done using PolyWorks MRS2019 IR3.

The Plane-to-Plane analysis showed a mean normal distance of 8mm (.32 inches) between the Elios 3 model and the reference model. The Range Noise analysis yielded a mean standard deviation of 8mm (.32 inches) to 1-sigma.

These results indicate that the Elios 3 with GeoSLAM Connect performs exceptionally well compared to traditional TLS systems and leading mobile mapping solutions like the ZEB Revo and ZEB Horizon. It demonstrates strong precision and local accuracy suitable for a wide range of industrial applications.

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