Localization in a battlefield environment is increasingly challenging as GPS
connectivity is often denied or unreliable, and physical deployment of anchor
nodes across wireless networks for localization can be difficult in hostile
battlefield terrain. Existing range-free localization methods rely on
radio-based anchors and their average hop distance which suffers from accuracy
and stability in dynamic and sparse wireless network topology. Vision-based
methods like SLAM and Visual Odometry use expensive sensor fusion techniques
for map generation and pose estimation. This paper proposes a novel framework
for localization in non-GPS battlefield environments using only the passive
camera sensors and considering naturally existing or artificial landmarks as
anchors. The proposed method utilizes a customcalibrated stereo vision camera
for distance estimation and the YOLOv8s model, which is trained and fine-tuned
with our real-world dataset for landmark recognition. The depth images are
generated using an efficient stereomatching algorithm, and distances to
landmarks are determined by extracting the landmark depth feature utilizing a
bounding box predicted by the landmark recognition model. The position of the
unknown node is then obtained using the efficient least square algorithm and
then optimized using the L-BFGS-B (limited-memory quasi-Newton code for
bound-constrained optimization) method. Experimental results demonstrate that
our proposed framework performs better than existing anchorbased DV-Hop
algorithms and competes with the most efficient vision-based algorithms in
terms of localization error (RMSE).

提出一种新的框架，利用仅使用被动相机传感器和作为锚点的自然存在或人工地标，实现在非 GPS 战场环境中的定位。该方法使用经过校准的双目相机进行距离估计，并使用基于我们的真实世界数据集训练和微调的 YOLOv8s 模型进行地标识别。通过提取地标深度特征，利用地标识别模型预测的边界框确定地标距离。然后，利用最小二乘法算法获得未知节点的位置，并使用 L-BFGS-B 方法进行优化。实验结果表明，我们提出的框架在定位误差 (RMSE) 方面优于现有的基于锚点的 DV-Hop 算法，并与效率最高的基于视觉的算法竞争。

基于立体视觉和深度学习的 GPS 缺失战场环境中的基准点定位

Landmark-based Localization using Stereo Vision and Deep Learning in  GPS-Denied Battlefield Environment

We present an efficient end-to-end pipeline for largescale landmark
recognition and retrieval. We show how to combine and enhance concepts from
recent research in image retrieval and introduce two architectures especially
suited for large-scale landmark identification. A model with deep orthogonal
fusion of local and global features (DOLG) using an EfficientNet backbone as
well as a novel Hybrid-Swin-Transformer is discussed and details how to train
both architectures efficiently using a step-wise approach and a sub-center
arcface loss with dynamic margins are provided. Furthermore, we elaborate a
novel discriminative re-ranking methodology for image retrieval. The
superiority of our approach was demonstrated by winning the recognition and
retrieval track of the Google Landmark Competition 2021.

我们提出了一种高效的端到端管道，用于大规模地标识别和检索，演示如何结合和增强最近图像检索研究中的概念并介绍两种特别适用于大规模地标识别的架构：深度正交融合的全局和局部特征模型（DOLG）使用 EfficientNet 骨干和一种新的混合 Swin 变换器，讨论了如何使用逐步方法高效地训练两种架构，并提供了一个具有动态边缘的子中心 Arcface 损失，此外还详细阐述了一种新的判别式重新排名方法用于图像检索，我们的方法通过赢得 Google Landmark 竞赛 2021 的识别和检索赛道证明了其卓越性。

具有深度特征正交性和混合 Swin 转换器的高效大规模图像检索

Efficient large-scale image retrieval with deep feature orthogonality  and Hybrid-Swin-Transformers

We propose an efficient pipeline for large-scale landmark image retrieval
that addresses the diversity of the dataset through two-stage discriminative
re-ranking. Our approach is based on embedding the images in a feature-space
using a convolutional neural network trained with a cosine softmax loss. Due to
the variance of the images, which include extreme viewpoint changes such as
having to retrieve images of the exterior of a landmark from images of the
interior, this is very challenging for approaches based exclusively on visual
similarity. Our proposed re-ranking approach improves the results in two steps:
in the sort-step, $k$-nearest neighbor search with soft-voting to sort the
retrieved results based on their label similarity to the query images, and in
the insert-step, we add additional samples from the dataset that were not
retrieved by image-similarity. This approach allows overcoming the low visual
diversity in retrieved images. In-depth experimental results show that the
proposed approach significantly outperforms existing approaches on the
challenging Google Landmarks Datasets. Using our methods, we achieved 1st place
in the Google Landmark Retrieval 2019 challenge and 3rd place in the Google
Landmark Recognition 2019 challenge on Kaggle. Our code is publicly available
here: https://github.com/lyakaap/Landmark2019-1st-and-3rd-Place-Solution

本文提出了一种通过两阶段鉴别性重新排列解决数据集多样性问题的大规模地标图像检索的有效流程，该方法基于使用余弦 softmax 损失训练的卷积神经网络嵌入特征空间中的图像，通过 $k$ 最近邻搜索和软投票对检索结果进行排序，然后通过从未通过图像相似性检索的数据集中添加附加样本的方式进行插入，以克服检索图像的低视觉多样性。经过深入的实验结果表明，所提出的方法在挑战性的 Google Landmarks 数据集上明显优于现有的方法。