UAV Photogrammetry: Precision and Challenges in Modern Surveying

Introduction

This literature review was undertaken to consolidate some of the information on the use of unmanned aerial vehicles to perform photogrammetric survey around the world.

Photogrammetric surveying is not a new process, traditionally being almost exclusively performed by fixed wing manned aircraft, equipped with sensors and cameras. Being disadvantaged by high cost, long processing times, and specialised hardware/software makes the consistent and frequent use of traditional airborne photogrammetry survey limited against the far more popular ground based theodolite survey.

Also, ground survey having the distinct advantage that it can be produced with a much higher level of confidence due to the types of equipment and controls used, but due to the nature of capture can only be produce over a small area in comparison to aerial photogrammetric survey.

Considerations Affecting Accuracy

Meteorological conditions area known variable in any aerial data collection and are likewise going to influence the accuracy of survey data, as well as impede on the data collection process itself.

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Any wind speed that affects the flight of the drone or alters the drone’s position or flight path could potentially effect the accuracy of the data collection. Moving the drone from its mathematically intended GPS position resulting in offset or misaligned photos which lead to errors in processioning (Sushchenko & Goncharenko 2016).

The calculation methodology and human collection of survey data is a well-documented source of error and uncertainty in survey accuracy. Calculation errors or data input error can account for many survey data collection inaccuracies (Schofield & Breach 2007).

This error and uncertainty remains applicable to photogrammetric survey and its associated practices, for this reason care must be taken to capture complete data sets, maintaining proper image overlap and through data management.

Geo-referencing photogrammetric survey into a projected coordinate system, like GDA94 as required by TMR (TMR Survey Standards 2016, TN156 2016) requires additional data processing. Ground control points capture by GPS ground survey and then use to georeferenced the drone survey, although not required to produce photogrammetric survey, are require to achieve accurate geo-referencing for the survey data point. (Barry et al. 2013). It has been shown that the as the density of the ground control points increase the survey data’s mean absolute error values decrease (Elena Ridolfi et al. 2017).

The targets as seen in the Orthomosaic. Note: the blackdot which represents where the centre of the target is according to the GPS ground survey.

Camera resolution and quality can influence the accuracy and precision of survey data when it comes to processing by automated software. Low resolution photos can lead to errors due to blurry, or reflective surfaces being misidentified or their miscalculated locations, (TN156 2016, Forlani et al. 2018, Anon 2017).

DJI Mavic Pro Specifications

• Max Flight Time 27 minutes (no wind at a consistent 15.5 mph (25 kph)) Operating Temperature Range 32° to 104° F (0° to 40° C)
• Max Hovering Time 24 minutes (no wind) Satellite Positioning Systems GPS / GLONASS
• Overall Flight Time 21 minutes ( In normal flight, 15% remaining battery level ) Sensor 1/2.3” (CMOS), Effective pixels:12.35 M (Total pixels:12.71M)
• Max Total Travel Distance (One Full Battery, No Wind) 8 mi (13 km, no wind) Lens FOV 78.8° 26 mm (35 mm format equivalent) f/2.2
• Distortion < 1.5% Focus from 0.5 m to ∞
• Max Speed 40 mph (65 kph) in Sport mode without wind Image Size 4000×3000
• Max Service Ceiling Above Sea Level 16404 feet (5000 m) Weight (Battery & Propellers Included) 1.62 lbs (734 g) (exclude gimbal cover)

Steep or highly changing topography can also be a challenge to accurately map using a small drone, as a drones navigation is based on its GPS and calculated height, if the software does not account for the change in verticality correctly and maintain a consistent distance from the ground the data collection of the drones vertical position becomes unreliable, introducing errors (Udin et al. 2014, Agüera-Vega et al. 2017, Cooper et al. 2015).

The ability for a drone to perform this maneuverer autonomously is not common in most small drones, as the stability of the drone for video capture is favoured above the ability keep the drone at a consistent height above the ground, therefore is often passed over in favour of completely independent flight control and data collection process. Steep terrain can similarly prevent the accurate survey to dense vegetation, where elevation or dense vegetation obstructs the field of view of nearby topography, reducing the amount of image overlap, limiting the processing software’s ability to see features from multiple angles to best triangulate their location to create the point cloud (Harwin et al. 2012)

Accuracies in Existing Research

A review of previous studies highlights varying degrees of accuracy achieved through UAV photogrammetry. The root-mean-square error (RMSE) serves as a standard measure of accuracy, with lower values indicating higher precision. The table below summarizes the accuracies reported in several studies, showcasing the potential of UAVs in achieving survey-grade accuracy.

Table: Accuracies of Previous Studies

The observed accuracies above are reported in RMSE (root mean squarer error), representing a degree of error or deviation from known values, i.e. ground control points. The accuracy range of these studies show the significant improvements gained by the use suitable methodology and equipment (Agüera-Vega 2017, Barry et al. 2013, Elena Ridolfi 2017, Norbert et al. 2017, Harwin et al. 2012)

Need for Additional Research

The variation in results from previous research, whilst they provide a good grounding for further research, the range of results only contribute to the perception that the sUAS cannot consistently achieve survey-grade accuracy (Hamilton, 2017). Additionally, it is frequently acknowledged that there is a need for continued research in previous studies (Remondino 2012, Cogliati et al. 2017, Oh et al. 2017)

As the focus of most of these reports isn’t accuracy, and the discussion of accuracy is often in addition to, or incidental to, the actual research then the accuracy and precision of the photogrammetric results can often be confused with and become synonymous with studies unrelated to the accuracy. Further study is required to determine if a small unmanned aircraft, or drone, and be used to achieve the ICSM and TMR relative uncertainties at a 95% confidence level (ICSM 2009, TMR 20016).

Conclusion

UAVs present a revolutionary approach to photogrammetric surveying, offering a blend of efficiency, coverage, and potential accuracy that traditional methods struggle to match. However, achieving consistent survey-grade accuracy requires addressing factors such as meteorological conditions, calculation methodologies, and geo-referencing. As UAV technology evolves, focused research is essential to harness its full potential in precision surveying.