Abstract
Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete products. The main limitation of these techniques is their inability to establish a sufficient number of correctly matched features among overlapping images, causing incomplete and/or inaccurate 3D reconstruction. This paper provides two structure from motion (SfM) strategies, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters. The main difference between the proposed strategies is that the first one—denoted as partially GNSS/INS-assisted SfM—implements the four stages of an automated triangulation procedure, namely, imaging matching, relative orientation parameters (ROPs) estimation, exterior orientation parameters (EOPs) recovery, and bundle adjustment (BA). The second strategy— denoted as fully GNSS/INS-assisted SfM—removes the EOPs estimation step while introducing a random sample consensus (RANSAC)-based strategy for removing matching outliers before the BA stage. Both strategies modify the image matching by restricting the search space for conjugate points. They also implement a linear procedure for ROPs’ refinement. Finally, they use the GNSS/INS information in modified collinearity equations for a simpler BA procedure that could be used for refining system calibration parameters. Eight datasets over six agricultural fields are used to evaluate the performance of the developed strategies. In comparison with a traditional SfM framework and Pix4D Mapper Pro, the proposed strategies are able to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.
Highlights
Feeding the growing population will be one of the most challenging tasks for agriculture in the near future [1]
Two frameworks, denoted as partially GNSS/INS-assisted structure from motion (SfM) and fully GNSS/INS-assisted SfM, have been introduced for reliable aerial triangulation of unmanned aerial vehicles (UAVs)-based images captured over agricultural fields
The key motivation for such development is mitigating the limitations of existing SfM strategies, namely, poor distribution of derived object points and significant gaps in generated orthophotos when working with large image blocks over mechanized agricultural fields
Summary
Feeding the growing population will be one of the most challenging tasks for agriculture in the near future [1]. The easier deployment and possibility of equipping UAVs with a variety of advanced imaging sensors, along with their capability to collect high temporal and spatial resolution data have increased the use of UAVs for digital agriculture, in applications such as phenotyping [9,10,11,12,13], crop monitoring [14,15,16,17], and yield estimation/prediction [18,19,20] For most of these applications, accurate georeferenced three-dimensional (3D) point clouds and orthophotos are the main products required for various plant trait measurements, such as canopy cover [21], plant height [22], and plant count [23,24]
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