Article of the Month - December 2020

A Toolset for Automating 1mm Measurement Accuracy in Photogrammetry Surveys

This article in .pdf-format (11 pages)


Ken Varner, Wolfgang Herbst, And David Schmidhuber, Germany


The implementation of calibrated photogrammetry tags produced with optimal surface materials enables very precise engineering surveys (down to 1mm relative measurement accuracy) of water management infrastructure.  Such photogrammetry tags create automatically detected control points within the model space that also provide the basis for very precise measurement control.

Each unique tag is coded within the photogrammetry software completely automating the typical manual work of adding control points, merging projects, and merging different datasets of the same project.  The nuance presented here is using the coded tags around the surveyed object as the measurement system.  Producing the tags with an optimized surface material enables the surveyor to mimic measurement standard conditions (lab conditions) in the field to systematically achieve sub-cm measurement accuracy.

The recipe for success for implementing this coded tag based measurement system as well as case studies of 3D surveys with 1mm measurement accuracy of stone infrastructure using Unmanned Aerial Vehicles (UAVs) and handheld cameras are presented here. The key elements to take into account for successful implementation of such surveys are the following:

  1. Know your camera

  2. Trust your measurement

  3. Control your space

  4. Understand your photogrammetry software

The correct implementation of photogrammetry tags with consumer grade measurement devices and consumer grade UAVs and cameras enables very precise and very fast photogrammetry based 3D models at a much lower cost compared to laser scanners and total stations.


The advent of consumer grade Unmanned Aerial Vehicles (UAVs) and multicopters weighing less than 2 kg has created a surge in new technologies specifically for surveying and inspection.  In particular, photogrammetry has achieved a wide spread market and is now considered a standard survey and inspection tool within these industries’ overall toolset.  Here, we present a toolset consisting of consumer grade equipment to systematically automate precision photogrammertry workflows.

The four main aspects that one must consider in order to obtain down to 1mm accurate measurement photogrammetry models are listed here and will be described more in depth within this paper:

  1. Know your camera

  2. Trust your measurement

  3. Control your space

  4. Understand how your photogrammetry software works

The two most commonly reported metrics in a calibrated photogrammetry surveys are pixel resolution (most commonly referred to as Ground Sampling Distance (GSD)), which determines the overall visual accuracy of the model, and the measurement accuracy, which can be presented in several ways. Even though the GSD has an effect on determining the baseline for measurement accuracy, GSD and measurement accuracy are controlled and determined completely independent of one another. The GSD is determined by the visual properties relating to the camera, distance from object, and focal length.  Measurement accuracy is determined by the measurement system used within the model space.


The main input for photogrammetry based surveys and inspections are images.  In this case, we are presenting results based on standard RGB imagery from consumer cameras.  The camera’s physical properties as well as the image distance from the model object are the key factors in calculating the GSD.  In Figure 1, the key parameters for the GSD calculation are defined.

Figure 1: The camera’s physical properties used for calculating GSD.

With these parameters defined, it is possible to now calculate a camera system’s GSD with the common formula below:

In common practice, one then selects the higher of the two values for determining the GSD.  In this project, a consumer grade Sony a5000 camera with an APS-C sensor was used with a 10 mm lens.  This camera system’s corresponding properties for photogrammetry applications are listed here: Using the calculations from above, we can calculate that a maximum distance of 2.33 m from the model object is necessary in order to maintain a GSD of 1 mm.


It can be very challenging for human beings to verify measurements in the real world, especially as the measurements get smaller across larger spaces.  For this reason, we rely on certified equipment such as laser distance measurement tools, Total Stations, and laser scanners to precisely measure the world around us.  Even though such devices are certified for accuracy, it is still the responsibility of the user to maintain the boundary conditions necessary to provide best accuracy while applying the proper techniques in the field.

For this work, we used a Leica Disto 810 for measuring at 1 mm accuracy.

Figure 2: Calibration certificate for a Leica Disto 810

The Calibration Certificate of Figure 2 shows that a “target plate albedo 1” was used to calibrate the device.  This now becomes the baseline for providing a measurement system around our model object. By creating diffuse reflective surfaces for targeting our DISTO device, we can achieve optimal surface conditions for precise measurements.


An important practice when performing precision photogrammetry is the use of coded targets (also known as tags or markers) that can be automatically registered in the photogrammetry software. Figure 3 shows an example of Chilitags[1] as they are automatically detected and registered within the photogrammetry software.  Coded targets are commonly used for automating the photogrammetry workflow and provide the following benefits: automatically generate tie points, automatically locate Ground Control Points, reduce time for manual processing steps, and seamlessly merge sub-projects.

Figure 3: Automatically detected coded targets within the Pix4D[2] software.

This concept is not new, but the nuance presented here is using the coded tags around the surveyed object as the measurement system. Producing the tags with an optimized surface material enables the surveyor to mimic measurement standard conditions (lab conditions) in the field to systematically achieve sub-cm measurement accuracy. The combination of precisely locating the coded targets’ corners while using an optimized surface material for precise measurements with the DISTO device enables 1 mm measurement precision for terrestrial photogrammetry projects.


The idea of ”controlling your space” is that much of the infrastructure being modelled with photogrammetry is not optimal for pulling precise measurements due to surfaces materials, shapes, or inaccessibility. By implementing coded targets printed on diffuse reflective surfaces around the infrastructure to be scanned, we create predictable points for pulling precise measurements independent of the conditions of the model space.  Figure 4 shows an example of 4 cubes being placed around a stone bridge creating a precise measurement system within the model space.  The points used for measurements are then automatically recognized within the photogrammetry software for model calibration.

Figure 4: Use of coded targets on aluminum cubes to create a predictable measurement space around the model object.


6.1 Stone Wall

A photogrammetry model of the stone wall in Figure 5 was contracted with the goal of finding the combiner stones that provide the mechanical stabilisation of such a wall.  These stones are single stones that traverse both sides of the stone preventing wall collapse over time. In order, to locate these stones, it was necessary to first, create a 1 mm accurate survey model of the structure and second, create calibrated, mirrored orthophotos of the two sides for a precise overlay of both sides of the wall in order to ”see through” the wall and determine which stones traverse the entire structure.

Figure 5: Final mesh of the stone wall within Pix4D.

Note the control points in green, which were automatically detected in the software, and the scale constraint across the top of the wall.

Figures 6 and 7 show the GSD and measurements results of the photogrammetry model, where a GSD of 1 mm was achieved and a measurement accuracy sigma of 0.6 mm over 27.160 m.

Figure 6: Quality Report of the photogrammetry model.

Figure 7: Quality Report of the photogrammetry model showing the errors of the compiled length measurements and scale constraints.

6.2 Stone Bridge

A photogrammetry survey of the stone bridge in Figure 8 as part of a renovation project.  The following results were achieved: GSD = 1.3 mm ; Measurement accuracy = ±1 mm.

Figure 8: Final mesh of a stone bridge

6.3 Dam Inspection

A photogrammetry survey of the dam in Figure 9 as part of a renovation project.  The following results were achieved: GSD = 1.7 mm ; Measurement accuracy = ±1.2 mm.

Figure 9: Photogrammetry model of a dam within Pix4D.

All green control points were automatically detected using coded targets.


  1. Chilitags: Robust Fiducial Markers for Augmented Reality. Q. Bonnard, S. Lemaignan, G. Zufferey, A. Mazzei, S. Cuendet, N. Li, P. Dillenbourg. CHILI, EPFL, Switzerland. 2013.



Dr. Ken Varner has a background in semiconductor measurement physics and has spent the last several years applying photogrammetry as a precision measurement tool in the built environment.  With several patents in the aerial inspection space, Ken’s focus is on automated precision of aerial inspection workflows.


Dr. Ken Varner Vaireco

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