Camera Self-Calibration with GNSS Constrained Bundle Adjustment for Weakly Structured Long Corridor UAV Images

Wei Huang,San Jiang,Wanshou Jiang

doi:10.3390/rs13214222

Abstract

Camera self-calibration determines the precision and robustness of AT (aerial triangulation) for UAV (unmanned aerial vehicle) images. The UAV images collected from long transmission line corridors are critical configurations, which may lead to the “bowl effect” with camera self-calibration. To solve such problems, traditional methods rely on more than three GCPs (ground control points), while this study designs a new self-calibration method with only one GCP. First, existing camera distortion models are grouped into two categories, i.e., physical and mathematical models, and their mathematical formulas are exploited in detail. Second, within an incremental SfM (Structure from Motion) framework, a camera self-calibration method is designed, which combines the strategies for initializing camera distortion parameters and fusing high-precision GNSS (Global Navigation Satellite System) observations. The former is achieved by using an iterative optimization algorithm that progressively optimizes camera parameters; the latter is implemented through inequality constrained BA (bundle adjustment). Finally, by using four UAV datasets collected from two sites with two data acquisition modes, the proposed algorithm is comprehensively analyzed and verified, and the experimental results demonstrate that the proposed method can dramatically alleviate the “bowl effect” of self-calibration for weakly structured long corridor UAV images, and the horizontal and vertical accuracy can reach 0.04 m and 0.05 m, respectively, when using one GCP. In addition, compared with open-source and commercial software, the proposed method achieves competitive or better performance.

Highlights

With the advantages of flexible data acquisition and ease of use, UAV has become one of the most important remote sensing platforms for the photogrammetry and remote sensing community [1]
Two datasets of long corridor transmission line UAV images were collected by DJI
There was a “bowl effect” with MicMac in the four datasets except for the S-shaped dataset in test site 2, while the bending of the reconstructed models was significantly reduced with Pix4d and the proposed method

Summary

Introduction

With the advantages of flexible data acquisition and ease of use, UAV has become one of the most important remote sensing platforms for the photogrammetry and remote sensing community [1]. The UAV platforms are often equipped with consumer-grade, non-metric digital cameras, mainly due to the limitations of the platform’s load capacity. These cameras have non-ignored lens distortions when compared with metric sensors, which influences the robustness and precision of AT. The long corridor structure of UAV images is a critical configuration, and the reconstructed model would be bending with the inaccurately estimated distortion parameters.

Methods

Results

Conclusion