UAV-Based Autonomous Image Acquisition with Multi-view Stereo Quality Assurance by Confidence Prediction

摘要

In this paper we present an autonomous system for acquiring close-range high-resolution images that maximizeudthe quality of a later-on 3D reconstruction with respect toudcoverage, ground resolution and 3D uncertainty. In contrastudto previous work, our system uses the already acquiredudimages to predict the confidence in the output of a denseudmulti-view stereo approach without executing it. This confidence encodes the likelihood of a successful reconstruction with respect to the observed scene and potential camera constellations. Our prediction module runs in real-time and can be trained without any externally recorded ground truth. We use the confidence prediction for on-site Quality assurance and for planning further views that are tailored for a specific multi-view stereo approach with respect to the given scene. We demonstrate the capabilities of our approach with an autonomous Unmanned Aerial Vehicle (UAV) in a challenging outdoor scenario.