面向非GPS战场环境中地标识别与移动节点定位的地标立体数据集
Landmark Stereo Dataset for Landmark Recognition and Moving Node Localization in a Non-GPS Battlefield Environment
AI总结：
该研究提出在GPS拒止战场中以视觉地标替代无线电锚节点，构建MSTLandmarkv1与MSTLandmarkStereov1，并结合YOLOv5和改进SGM生成地标ID与距离虚拟坐标。
摘要：
本文提出了一种使用地标锚节点替代基于无线电的锚节点的新策略，以获取移动部队或防御部队的虚拟坐标（landmarkID，DISTANCE），这将有助于在GPS拒止战场环境中沿安全路径跟踪和机动部队。所提策略使用Yolov5模型实现地标识别，并使用高效的立体匹配算法进行地标距离估计。我们假设移动节点携带低功耗移动设备，该设备配备经过标定的立体视觉相机，可捕获战场区域内包含地标的场景立体图像，而这些地标的位置存储在设备自身的离线服务器中。我们创建了一个包含34个地标类别的自定义地标图像数据集MSTLandmarkv1，以及另一个包含这34个地标实例的地标立体数据集MSTLandmarkStereov1。我们使用MSTLandmarkv1数据集训练YOLOv5模型，取得了0.95 mAP @ 0.5 IoU和0.767 mAP @ [0.5:0.95] IoU。我们利用边界框坐标以及改进的SGM算法在MSTLandmarkStereov1上生成的深度图，计算节点到地标的距离。从检测结果获得的地标ID元组与SGM算法计算出的距离一起存储为节点的虚拟坐标。在未来工作中，我们将使用这些虚拟坐标，通过高效的三边测量算法获得节点位置，并采用适当的优化方法优化节点位置。
英文摘要：
In this paper, we have proposed a new strategy of using the landmark anchor node instead of a radio-based anchor node to obtain the virtual coordinates (landmarkID, DISTANCE) of moving troops or defense forces that will help in tracking and maneuvering the troops along a safe path within a GPS-denied battlefield environment. The proposed strategy implements landmark recognition using the Yolov5 model and landmark distance estimation using an efficient Stereo Matching Algorithm. We consider that a moving node carrying a low-power mobile device facilitated with a calibrated stereo vision camera that captures stereo images of a scene containing landmarks within the battlefield region whose locations are stored in an offline server residing within the device itself. We created a custom landmark image dataset called MSTLandmarkv1 with 34 landmark classes and another landmark stereo dataset of those 34 landmark instances called MSTLandmarkStereov1. We trained the YOLOv5 model with MSTLandmarkv1 dataset and achieved 0.95 mAP @ 0.5 IoU and 0.767 mAP @ [0.5: 0.95] IoU. We calculated the distance from a node to the landmark utilizing the bounding box coordinates and the depth map generated by the improved SGM algorithm using MSTLandmarkStereov1. The tuple of landmark IDs obtained from the detection result and the distances calculated by the SGM algorithm are stored as the virtual coordinates of a node. In future work, we will use these virtual coordinates to obtain the location of a node using an efficient trilateration algorithm and optimize the node position using the appropriate optimization method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
