Title: GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

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 removingmore » 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.« less

Authors:

Hasheminasab, Seyyed Meghdad ;

• Search DOE PAGES for author "Hasheminasab, Seyyed Meghdad"
• Search DOE PAGES for ORCID "0000-0003-3647-1480"
• Search orcid.org for ORCID "0000-0003-3647-1480"

Zhou, Tian ;

• Search DOE PAGES for author "Zhou, Tian"
• Search DOE PAGES for ORCID "0000-0001-6423-4090"
• Search orcid.org for ORCID "0000-0001-6423-4090"

Habib, Ayman

• Search DOE PAGES for author "Habib, Ayman"
• Search DOE PAGES for ORCID "0000-0001-6498-5951"
• Search orcid.org for ORCID "0000-0001-6498-5951"

Publication Date:

2020-01-21

Research Org.:

Purdue Univ., West Lafayette, IN (United States)

Sponsoring Org.:

USDOE Advanced Research Projects Agency - Energy (ARPA-E)

OSTI Identifier:

1592811

Alternate Identifier(s):

OSTI ID: 1799143

Grant/Contract Number:  

AR0000593; AR0001135

Resource Type:

Published Article

Journal Name:

Remote Sensing

Additional Journal Information:

Journal Name: Remote Sensing Journal Volume: 12 Journal Issue: 3; Journal ID: ISSN 2072-4292

Publisher:

MDPI AG

Country of Publication:

Switzerland

Language:

English

Subject:

54 ENVIRONMENTAL SCIENCES; 47 OTHER INSTRUMENTATION; Environmental Sciences & Ecology; Geology; Remote Sensing; Imaging Science & Photographic Technology; structure from motion; automatic aerial triangulation; unmanned aerial vehicles; GNSS/INS-assisted mapping; image matching; epipolar geometry