Drone Referring Localization: An Efficient Heterogeneous Spatial Feature Interaction Method For UAV Self-Localization

• URL: http://arxiv.org/abs/2208.06561v3
• Date: Wed, 28 Aug 2024 04:36:23 GMT
• Title: Drone Referring Localization: An Efficient Heterogeneous Spatial Feature Interaction Method For UAV Self-Localization
• Authors: Ming Dai, Enhui Zheng, Jiahao Chen, Lei Qi, Zhenhua Feng, Wankou Yang,
• Abstract summary: We propose an efficient heterogeneous spatial feature interaction method, termed Drone Referring localization (DRL) Unlike conventional methods that treat different data sources in isolation, DRL facilitates the learnable interaction of heterogeneous features. Compared to traditional IR methods, DRL achieves superior localization accuracy (MA@20 +9.4%) while significantly reducing computational time (1/7) and storage overhead (2/3)
• Score: 22.94589565476653
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Image retrieval (IR) has emerged as a promising approach for self-localization in unmanned aerial vehicles (UAVs). However, IR-based methods face several challenges: 1) Pre- and post-processing incur significant computational and storage overhead; 2) The lack of interaction between dual-source features impairs precise spatial perception. In this paper, we propose an efficient heterogeneous spatial feature interaction method, termed Drone Referring Localization (DRL), which aims to localize UAV-view images within satellite imagery. Unlike conventional methods that treat different data sources in isolation, followed by cosine similarity computations, DRL facilitates the learnable interaction of heterogeneous features. To implement the proposed DRL, we design two transformer-based frameworks, Post-Fusion and Mix-Fusion, enabling end-to-end training and inference. Furthermore, we introduce random scale cropping and weight balance loss techniques to augment paired data and optimize the balance between positive and negative sample weights. Additionally, we construct a new dataset, UL14, and establish a benchmark tailored to the DRL framework. Compared to traditional IR methods, DRL achieves superior localization accuracy (MA@20 +9.4\%) while significantly reducing computational time (1/7) and storage overhead (1/3). The dataset and code will be made publicly available. The dataset and code are available at \url{https://github.com/Dmmm1997/DRL} .

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