VGF-Net: Visual-Geometric Fusion Learning for Simultaneous Drone Navigation and Height Mapping
Not on the map. Why?
Abstract (arXiv)
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. The dataset can be found in http://vcc.szu.edu.cn/research/2021/VGFNet.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 7 Apr 2021.
Builds on
19 of 31 references.
References · 19
- 2016Unsupervised Monocular Depth Estimation with Left-Right Consistencycited by 820
- 2016ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Camerascited by 806
- 2016Direct Sparse Odometrycited by 485
- 2019Learning by Cheatingcited by 383
- 2016Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learningcited by 366
- 2019What Do Single-view 3D Reconstruction Networks Learn?cited by 260
- 2019Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Videocited by 244
- 2017Cognitive Mapping and Planning for Visual Navigationcited by 212
- 2018Semi-parametric Topological Memory for Navigationcited by 209
- 2017Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Imagecited by 161
- 2017CNN-SLAM: Real-time dense monocular SLAM with learned depth predictioncited by 152
- 2017Semi-Supervised Deep Learning for Monocular Depth Map Predictioncited by 109
7 more
- 2018Learning to Navigate in Cities Without a Mapcited by 104
- 2017Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigationcited by 98
- 2019Combining Optimal Control and Learning for Visual Navigation in Novel Environmentscited by 52
- 2017Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Camerascited by 45
- 2019A Behavioral Approach to Visual Navigation with Graph Localization Networkscited by 40
- 2020ClusterVO: Clustering Moving Instances and Estimating Visual Odometry for Self and Surroundingscited by 17
- 2015Deep Neural Network for Real-Time Autonomous Indoor Navigationcited by 9
Built on by
Citing papers per year: 2021: 1; 2022: 2.
3 papers.