Drone navigation
12 papers with code • 1 benchmarks • 1 datasets
(Satellite -> Drone) Given one satellite-view image, the drone intends to find the most relevant place (drone-view images) that it has passed by. According to its flight history, the drone could be navigated back to the target place.
Most implemented papers
University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i. e., drone-view target localization and drone navigation.
Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations
We analyze the rich latent spaces learned with our proposed representations, and show that the use of our cross-modal architecture significantly improves control policy performance as compared to end-to-end learning or purely unsupervised feature extractors.
Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching
Navigating drones through natural language commands remains challenging due to the dearth of accessible multi-modal datasets and the stringent precision requirements for aligning visual and textual data.
Drone Path-Following in GPS-Denied Environments using Convolutional Networks
his paper presents a simple approach for drone navigation to follow a predetermined path using visual input only without reliance on a Global Positioning System (GPS).
Straight to Shapes++: Real-time Instance Segmentation Made More Accurate
The STS model can run at 35 FPS on a high-end desktop, but its accuracy is significantly worse than that of offline state-of-the-art methods.
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas.
FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation
Specifically, FBSNet employs a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch.
A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
However it still has some limitations, e. g., it can only extract part of the information in the neighborhood and some scale reduction operations will make some fine-grained information lost.
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning
Enabling the capability of assessing risk and making risk-aware decisions is essential to applying reinforcement learning to safety-critical robots like drones.
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localization
Inspired by the human visual system for mining local patterns, we propose a new framework called RK-Net to jointly learn the discriminative Representation and detect salient Keypoints with a single Network.