Active Iterative Optimization for Aerial Visual Reconstruction of Wide-Area Natural Environment

Abstract

Autonomous, accurate, and dynamic 3-D reconstruction for wide-area environments is crucial for unmanned aerial vehicle monitoring and rescue tasks, however, when conducted in an unknown complex terrain, the reconstruction result obtained from a single flight suffers poor quality. In this article, we present an Active Iterative Optimization framework for trajectory planning and visual reconstruction. Firstly, the trajectory is planned under the photogrammetric constraints based on rough terrain. Due to the visual field deviation caused by pose error during actual flight, the view loss evaluation is established and keyframes are selected to conduct 3-D reconstruction. A comprehensive metric is designed to quantitatively evaluate reconstruction effect without ground truth. The point cloud is then rasterized and divided into normal or low-scoring region according to the evaluation metric. In the next iteration, trajectory is replanned in low-scoring region to purposefully optimize the point cloud of local area. Thus the reconstruction result can be iteratively optimized. We validated the effectiveness of the proposed framework in simulation and physical experiments.