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Survey Drone vs. Scanning Total Station: A Gravel Pit Comparison

How do two of the world’s most cutting-edge surveying instruments stack up against each other? Are there obvious benefits to choosing one over the other? A new whitepaper from survey company Lerch Weber AG seeks to answer those questions and more. 

Earlier this year, Armin Weber and Thomas Lerch, co-owners of Swiss survey specialists Lerch Weber AG, set out to compare two cutting-edge surveying instruments: Trimble’s SX10 scanning total station and the senseFly eBee Plus survey drone with built-in RTK/PPK enabled and a senseFly S.O.D.A. RGB camera.

By comparing both tools, Armin and Thomas hoped to learn which instrument was best suited to projects such as gravel pit surveys, by comparing each products’ in-office preparation time, in-field data collection time, total data processing time and the quality of the point clouds each instrument produced. They also wanted to explore what, if any, visual outputs were generated by each instrument, assess the potential on-site risks that using each instrument posed to operators, and compare the relative cost of these technologies.

The perfect location

For their study, Armin and Thomas surveyed a four-hectare (about 10 acres) gravel pit in Switzerland. This was considered an ideal test location thanks to its deep floor (approx. 40 m/ 131 ft) and because it featured horizontal, vertical and overhanging sections.

The project’s measurements were taken by four Lech Weber AG employees, with a senseFly engineer also on hand to provide support.

The project site was a four-hectare (about 10 acres) gravel pit located near Olten, Switzerland.

Five individual point clouds were generated in total: four UAV point clouds, which came from two UAV flights flown at different heights above ground level, and one merged laser scanner point cloud from five stations.

The point clouds captured were then compared among several criteria, including the time it took to collect all the data in the field, the total data processing time and the positional accuracy, density and quality of the two instruments’ point clouds.

To georeference the laser scanning and to assess the accuracy of the eBee’s flights, nine ground control points (GCPs) were measured using a Trimble R10 GNSS receiver, which was evenly distributed over the entire gravel pit.

Lerch Weber’s senseFly eBee Plus UAV (ground left) and Trimble SX10 scanning total station (centre/right).

The GCPs were then marked on the terrain with 50 cm-wide square yellow plastic boards for greater visibility and to ensure that they could be properly identified later and marked in the eBee’s digital imagery.

Setting the nine GCPs, which were used for both the TLS and UAV surveys, took approximately 1.5 hours. The GCP points and the point cloud derived the laser scanner was measured in the Swiss national coordinate system CH1903+/LV95 and the national levelling system LN02.

Meanwhile, the eBee Plus flight was carried out in the WGS84 coordinate system and later transformed to the Swiss national coordinate system using Agisoft’s PhotoScan photogrammetry software, which was also used to process the drone’s imagery.

Planning and flight parameters

Pre-planning for the eBee Plus UAV flight took place before reaching the gravel pit with the eBee Plus’ eMotion pre-flight software. Armin and Thomas used a satellite background map, which was loaded within eMotion, then a polygon was drawn around the site, leaving a few meters of additional coverage outside the pit’s survey perimeter.

Once the trajectories of the flight had been decided, Armin and Thomas went about setting two key flight parameters: the required ground sampling distance (GSD), in centimetres per pixel, and the required image overlap percentage (lateral and longitudinal).

Armin and Thomas decided to fly the eBee Plus UAV twice, at different heights, to assess the influence of ground resolution on the quality of the drone’s point cloud outputs.

Screenshot of a 100 meter / no GCP UAV point cloud.

Because the eBee Plus can receive RTK corrections, and to enhance the prevision of the image geotags, Armin, Thomas and their team used a VRS RTK correction stream from Swisstopo. This required a Swisstopo service subscription and a network connection in the field (via the field laptop’s internet connection).

To allow for RTK precision of the UAV’s images, the radio link between the UAV and the ground station always had to be maintained. Armin and Thomas explain that had this radio link, or the laptop’s internet connection been disconnected, there would, however, still have been the possibility of applying corrections to the flight via the drone’s PPK capability.

Pre-site prep

Of course, the Trimble SX10 required its own pre-on-site preparation. This involved site analysis to estimate the optimal distribution of the project’s GCPs and laser scanner stations, with each station needing to provide line-of-sight access to at least three GCPs. Because of their knowledge of the site’s terrain, the process of placing the GCPs took roughly 15 minutes.

Once marking and measuring the project’s nine GCPs was complete, Armin, Thomas and their team went about setting up the SX10 at the first of its five stations. To orientate and set the exact position of the laser scanner, instrument levelling was required, after which a ‘free station’ methodology was used (a method of determining the 3D-location of one unknown point in relation to known points. In this case, three preset GCPs).

From there, the setup of the SX10 at each of its five stations took 15 minutes. This involved the scanner operator deciding upon which GCPs to target, and a second operator standing at each of these known points in turn and holding a target. The laser scanning was then carried out using the SX10’s default point density setting of Medium. The scanning time required at each of the five stations depended upon the width of the area being scanned, which was selected directly on the SX10’s screen.

Despite their familiarity with the site and having plenty of staff on hand, Armin and Thomas found that setting up the Trimble SX10 and performing laser scanning with the instrument took 45 minutes at each station.

In total, the team spent roughly 3 hours and 45 minutes—give or a take a few minutes—for the operators to travel to each scanning station.

Processing the data

Once the data was collected it had to be processed, with the process itself involving two steps: retrieving the images from the UAV flights to create four digital point clouds and copying the laser scanner’s point cloud from the five stations, which was rolled into a single file, onto a PC.

The eBee Plus’ flight data was processed using Agisoft PhotoScan photogrammetry software. In addition to the software generating the point cloud for each flight, it also generated an orthomosaic (a high-resolution aerial image of the site).

Meanwhile, for the scanning total station, and because the points gathered were already consolidated into a single point cloud, there was no remaining processing work to be done with the data from the laser scanner.

Comparing point clouds

With two UAV flights carried out at different heights, and GCPs set across the survey site, Armin and Thomas found that their project presented a unique opportunity to produce and compare several different UAV point clouds, which would allow the following questions to be answered: can an RTK flight alone achieve GCP levels of accuracy? What is the impact of flight height/GSD on point cloud quality? And, what effect does the number of photos have on point cloud density?

Details of the four UAV point clouds generated following the project’s two eBee Plus flights.

Relative vs. absolute accuracy

As Armin and Thomas point out in their whitepaper, when it comes to referencing the accuracy of terrestrial scanning and UAV, the topic is best divided into relative and absolute accuracy. The latter can be achieved with a UAV via the use of GCPs, while the former depends on the resolution of the drone’s imagery, which they found is linked directly to the aircraft’s flight height—the lower the height, the higher the point cloud densification.

Meanwhile, the absolute accuracy of a terrestrial laser scanner (TLS) depends on the method used to position the instrument. In the case of Armin and Thomas’ project, this was from the free station, meaning it was derived directly from the accuracy of the three GCPs measured to determine the position of each TLS station. The relative accuracy of the points measured with the TLS is directly correlated to the angular accuracy and the accuracy of the Electronic Distance Measurement (EDM).

The map of the area used for comparison

Armin and Thomas found that because the relative and absolute accuracy of the drone is off by a few centimeters, and the TLS can achieve an accuracy of a few millimeters, then we can assume that the a priori accuracy of the point cloud derived from a TLS is higher. For this reason—and because TLS collection achieves a higher density of points than UAV collection (at the TLS’s Medium density setting)—it was decided to treat the SX10-derived point cloud as the reference, against which to compare the different UAV point clouds.

Two excellent tools  

While it might seem preferential to have a clear-cut winner of their comparison study, Armin and Thomas found both the scanning total station and the survey drone to be excellent surveying tools. Where one proves better than the other largely depends on the job in question. For surveying projects where the very highest level of detail is essential, such as digital preservation projects on small sites, Armin and Thomas found that a laser scanner is optimal. However, for larger surveying projects, such as the gravel pit, quarry or construction site, an RTK-enabled UAV is more than acceptable, and provides acceptable levels of point cloud detail and accuracy, alongside greater efficiency.

However, considerations must also be made regarding worker safety, as drones allow for greater access to areas that would otherwise be difficult to reach and traverse, especially within a work site like a mine or quarry.

For this particular project, Armin and Thomas found that the optimal data collection approach—in terms of balancing efficiency and result quality—was the RTK UAV (without GCPs), flown at a low altitude. According to their findings, this approach achieved the shortest image processing time, high absolute accuracy and acceptable point density—all while minimising onsite risk, since the drone can be launched and landed outside the main pit. Armin and Thomas also noted that the retail cost of the eBee Plus, with RTK/PPK enabled, was approximately one-third that of the TLS in Switzerland.

For a detailed look at Armin and Thomas’ research, including their findings, download the whitepaper.

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