论文标题
带有参考视图渲染的深度无人机本地化
Deep UAV Localization with Reference View Rendering
论文作者
论文摘要
本文介绍了一个在深度学习的帮助下，在非结构化环境中定位无人机（UAV）的框架。引入了一个实时渲染引擎，该引擎会产生光学和深度图像，并给出六个自由度（DOF）相机姿势，相机型号，地理参考的矫形图和高程图。渲染引擎嵌入了基于学习的六道逆成分Lucas-kanade（ICLK）算法中，该算法能够牢固地对齐无人机拍摄的渲染和现实世界图像。为了了解环境变化的一致性，使用跨越多年的地图进行了培训。评估表明，深6DOF-ICLK算法的表现优于其不可训练的对应物。为了进一步支持该领域的研究，将发布实时渲染引擎和随附的数据集以及该出版物。
This paper presents a framework for the localization of Unmanned Aerial Vehicles (UAVs) in unstructured environments with the help of deep learning. A real-time rendering engine is introduced that generates optical and depth images given a six Degrees-of-Freedom (DoF) camera pose, camera model, geo-referenced orthoimage, and elevation map. The rendering engine is embedded into a learning-based six-DoF Inverse Compositional Lucas-Kanade (ICLK) algorithm that is able to robustly align the rendered and real-world image taken by the UAV. To learn the alignment under environmental changes, the architecture is trained using maps spanning multiple years at high resolution. The evaluation shows that the deep 6DoF-ICLK algorithm outperforms its non-trainable counterparts by a large margin. To further support the research in this field, the real-time rendering engine and accompanying datasets are released along with this publication.
