基于立体视觉与深度学习的GPS拒止战场环境地标定位
Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment
AI总结：
本文提出一种GPS拒止战场环境下仅用被动立体相机和深度学习地标识别的定位框架，通过YOLOv8s识别地标、立体匹配估计距离，并结合最小二乘与L-BFGS-B优化位置，实验表明其优于DV-Hop算法。
摘要：
在战场环境中，由于GPS连接经常被拒止或不可靠，且在敌对战场地形中跨无线网络物理部署锚节点十分困难，定位问题日益严峻。现有的免测距定位方法依赖基于无线电的锚节点及其平均跳距，在动态和稀疏的无线网络拓扑中精度和稳定性较差。基于视觉的方法如SLAM和视觉里程计使用昂贵的传感器融合技术进行地图生成和姿态估计。本文提出了一种在无GPS战场环境中仅使用被动相机传感器并将自然存在或人工地标视为锚节点的新型定位框架。所提方法利用定制标定的立体视觉相机进行距离估计，并使用YOLOv8s模型进行地标识别，该模型使用我们真实世界的数据集进行训练和微调。深度图像通过高效的立体匹配算法生成，利用地标识别模型预测的边界框提取地标深度特征来确定到地标的距离。随后使用高效的最小二乘算法获得未知节点的位置，并使用L-BFGS-B（用于有界约束优化的有限记忆拟牛顿法）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节点的DV-Hop算法，并能与最高效的基于视觉的算法相竞争。
英文摘要：
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).
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