Pose Error Reduction for Focus Enhancement in Thermal Synthetic Aperture Visualization

TL;DR

This paper introduces a novel approach to improve drone pose accuracy in thermal synthetic aperture imaging by framing pose estimation as a focusing problem, leading to better image quality and efficient processing.

Contribution

It proposes a new optimization method that reduces pose errors and enhances image focus, surpassing traditional Perspective-n-Point solutions.

Findings

Improved synthetic aperture images with clearer artifacts.

Reduced pose estimation errors compared to conventional methods.

Faster processing times due to optimized parameter search.

Abstract

Airborne optical sectioning, an effective aerial synthetic aperture imaging technique for revealing artifacts occluded by forests, requires precise measurements of drone poses. In this article we present a new approach for reducing pose estimation errors beyond the possibilities of conventional Perspective-n-Point solutions by considering the underlying optimization as a focusing problem. We present an efficient image integration technique, which also reduces the parameter search space to achieve realistic processing times, and improves the quality of resulting synthetic integral images.