Evaluating the Performance of sUAS Photogrammetry with PPK Positioning for Infrastructure Mapping

Conor Mcmahon,Michael J Starek,Omar E Mora

doi:10.3390/drones5020050

Abstract

Traditional acquisition methods for generating digital surface models (DSMs) of infrastructure are either low resolution and slow (total station-based methods) or expensive (LiDAR). By contrast, photogrammetric methods have recently received attention due to their ability to generate dense 3D models quickly for low cost. However, existing frameworks often utilize many manually measured control points, require a permanent RTK/PPK reference station, or yield a reconstruction accuracy too poor to be useful in many applications. In addition, the causes of inaccuracy in photogrammetric imagery are complex and sometimes not well understood. In this study, a small unmanned aerial system (sUAS) was used to rapidly image a relatively even, 1 ha ground surface. Model accuracy was investigated to determine the importance of ground control point (GCP) count and differential GNSS base station type. Results generally showed the best performance for tests using five or more GCPs or when a Continuously Operating Reference Station (CORS) was used, with vertical root mean square errors of 0.026 and 0.027 m in these cases. However, accuracy outputs generally met comparable published results in the literature, demonstrating the viability of analyses relying solely on a temporary local base with a one hour dwell time and no GCPs.

Highlights

Numerous disciplines require the generation of three-dimensional models, point clouds, or maps
The methods using PPK all produced very similar estimates for the six camera parameters, while the methods utilizing only ground control point (GCP) yielded a different set of mutually similar values
The three ki parameter terms which allow for correction of radial distortion were more negative for the PPK approaches when compared to the GCP approaches

Summary

Introduction

Numerous disciplines require the generation of three-dimensional models, point clouds, or maps. In civil engineering, this information is required to monitor, in real time, the dimensions of buildings [1], tunnels [2], and materials stockpiles [3] during construction, or to validate their dimensions once finished. Three-dimensional models are increasingly used in agriculture and forestry [9,10] and in the environmental sciences [9,11,12,13,14,15,16,17,18,19,20,21]. The required model accuracy and resolution varies substantially across applications. For some construction-monitoring applications a deviation of 0.01 m may be seen as substantial [2], while many other applications require an accuracy somewhere in between these values

Methods

Results

Discussion

Conclusion