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computer-vision-2

Drone navigation

3260 papers • 126 benchmarks • 313 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.

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University-1652

University-1652

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University-1652

University-1652

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Most implemented papers

University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization

Yunchao Wei, Zhedong Zheng, Yi Yang•Wed Feb 26 2020

It is argued that drones could serve as the third platform to deal with the geo-localization problem and propose a strong CNN baseline on this challenging dataset, named University-1652, which is the first drone-based geo- localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

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Drone Path-Following in GPS-Denied Environments using Convolutional Networks

M. Samy, K. Amer, M. Shaker, M. Elhelw•Sat May 04 2019

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), and suggests possible enhancements for extending the approach to more difficult paths in real life.

4 0

Learning Visuomotor Policies for Aerial Navigation Using Cross-Modal Representations

Ashish Kapoor, Vibhav Vineet, S. Scherer, Rogerio Bonatti, Ratnesh Madaan•Sun Sep 15 2019

This work proposes a novel method for learning robust visuomotor policies for real-world deployment which can be trained purely with simulated data, and develops rich state representations that combine supervised and unsupervised environment data.

45 0

Each Part Matters: Local Patterns Facilitate Cross-View Geo-Localization

Zhedong Zheng, Yi Yang, C. Yan, Yaoqi Sun, Jiyong Zhang, Bolun Zheng, Tingyu Wang•Tue Aug 25 2020

It is argued that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization, and introduced a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner.

241 0

Straight to Shapes++: Real-time Instance Segmentation Made More Accurate

Philip H. S. Torr, S. Golodetz, Saumya Jetley, Laurynas Miksys, Michael Sapienza•Sun May 26 2019

This work extends an existing approach to real-time instance segmentation, called `Straight to Shapes' (STS), which makes use of low-dimensional shape embedding spaces to directly regress to object shape masks, and finds that parameter sharing, more aggressive data augmentation and the use of structured loss for shape mask prediction all provide a useful boost to the network performance.

7 0

FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation

Jian Yang, Yi Yu, Guangwei Gao, Guoan Xu, Juncheng Li, Huimin Lu•Wed Sep 01 2021

This article proposes a Fast Bilateral Symmetrical Network (FBSNet), a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch that can strike a good balance between accuracy and efficiency.

113 0

A Transformer-Based Feature Segmentation and Region Alignment Method for UAV-View Geo-Localization

Enhui Zheng, Jiedong Zhuang, Ming Dai, Jian Hu•Sat Jan 22 2022

A simple and efficient transformer-based structure called Feature Segmentation and Region Alignment (FSRA) is introduced to enhance the model’s ability to understand contextual information as well as to understand the distribution of instances and achieves the state-of-the-art in both tasks of drone view target localization and drone navigation.

184 0

Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement Learning

Cheng Liu, E. Kampen, G. D. Croon•Sun Mar 27 2022

This paper investigates a specific case where a nano quadcopter robot learns to navigate an apriori-unknown cluttered environment under partial observability and presents a distributional reinforcement learning framework to generate adaptive risk-tendency policies.

23 0

Joint Representation Learning and Keypoint Detection for Cross-View Geo-Localization

N. Sebe, Zhedong Zheng, Yi Yang, Zhun Zhong, Shaozi Li, Zhiming Luo, Jinliang Lin•Sun May 22 2022

In this paper, we study the cross-view geo-localization problem to match images from different viewpoints. The key motivation underpinning this task is to learn a discriminative viewpoint-invariant visual representation. 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. Specifically, we introduce a Unit Subtraction Attention Module (USAM) that can automatically discover representative keypoints from feature maps and draw attention to the salient regions. USAM contains very few learning parameters but yields significant performance improvement and can be easily plugged into different networks. We demonstrate through extensive experiments that (1) by incorporating USAM, RK-Net facilitates end-to-end joint learning without the prerequisite of extra annotations. Representation learning and keypoint detection are two highly-related tasks. Representation learning aids keypoint detection. Keypoint detection, in turn, enriches the model capability against large appearance changes caused by viewpoint variants. (2) USAM is easy to implement and can be integrated with existing methods, further improving the state-of-the-art performance. We achieve competitive geo-localization accuracy on three challenging datasets, i. e., University-1652, CVUSA and CVACT. Our code is available at https://github.com/AggMan96/RK-Net.

121 0

Physical Passive Patch Adversarial Attacks on Visual Odometry Systems

A. Bronstein, Chaim Baskin, Yaniv Nemcovsky, Matan Yaakoby•Sun Jul 10 2022

It is shown for the first time that the error margin of a visual odometry model can be significantly increased by deploying patch adversarial attacks in the scene, and it is demonstrated that a comparable vulnerability exists in real data.

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Drone navigation | State-of-the-Art