VGF-Net: Visual-Geometric fusion learning for simultaneous drone navigation and height mapping

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models Pub Date : 2021-07-01 DOI:10.1016/j.gmod.2021.101108

Yilin Liu, Ke Xie, Hui Huang

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引用次数: 14

Abstract

The drone navigation requires the comprehensive understanding of both visual and geometric information in the 3D world. In this paper, we present a Visual-Geometric Fusion Network (VGF-Net), a deep network for the fusion analysis of visual/geometric data and the construction of 2.5D height maps for simultaneous drone navigation in novel environments. Given an initial rough height map and a sequence of RGB images, our VGF-Net extracts the visual information of the scene, along with a sparse set of 3D keypoints that capture the geometric relationship between objects in the scene. Driven by the data, VGF-Net adaptively fuses visual and geometric information, forming a unified Visual-Geometric Representation. This representation is fed to a new Directional Attention Model (DAM), which helps enhance the visual-geometric object relationship and propagates the informative data to dynamically refine the height map and the corresponding keypoints. An entire end-to-end information fusion and mapping system is formed, demonstrating remarkable robustness and high accuracy on the autonomous drone navigation across complex indoor and large-scale outdoor scenes.

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VGF-Net:用于无人机导航和高度映射的视觉几何融合学习

无人机导航需要对三维世界的视觉和几何信息有全面的了解。在本文中，我们提出了一个视觉几何融合网络(VGF-Net)，这是一个用于视觉/几何数据融合分析和构建2.5D高度地图的深度网络，用于在新环境中同时进行无人机导航。给定初始的粗略高度图和RGB图像序列，我们的VGF-Net提取场景的视觉信息，以及捕获场景中物体之间几何关系的3D关键点的稀疏集。在数据的驱动下，VGF-Net自适应融合视觉和几何信息，形成统一的视觉几何表示。将这种表示形式输入到新的定向注意模型(DAM)中，增强视觉与几何对象的关系，并传播信息数据，从而动态细化高度图和相应的关键点。形成了完整的端到端信息融合和制图系统，在复杂的室内和大型室外场景下，对自主无人机导航具有显著的鲁棒性和高精度。

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来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.