DETERMINATION OF UAS TRAJECTORY IN A KNOWN ENVIRONMENT FROM FPV VIDEO
DETERMINATION OF UAS TRAJECTORY IN A KNOWN ENVIRONMENT FROM FPV VIDEO
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根据 FPV 视频确定已知环境中的 UAS 轨迹
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
C. Armenakis
中科院分区：
文献类型：
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作者：
J. Li;C. Armenakis
提出了一种新的自定位方法。该算法自动建立从结构化城市环境中飞行的无人机传输的FPV视频与其3D模型之间的对应关系。由此产生的相机姿势在拥挤的环境中提供了精确的导航解决方案。最初，垂直线特征是从流的FPV视频帧中提取的，因为摄像机通过万向架系统保持大致水平。然后将这些特征与从3D模型的合成图像中提取的垂直线特征进行匹配。执行空间切除以提供该框架的EOPS。在下一帧中跟踪这些要素，然后进行增量三角剖分。本文的主要贡献在于将这一过程自动化，因为FPV视频序列通常由数千帧组成。给出了摄像机位置和姿态参数的精度，并对估计参数进行了验证。未来的工作包括实时测试该方法，以确定延迟和可靠性，以及FPV摄像机的多方向视场。
This paper presents a novel self-localization method. The algorithm automatically establishes correspondence between the FPV video streamed from a UAS flying in a structured urban environment and its 3D model. The resulting camera pose provides a precise navigation solution in the densely crowded environment. Initially, Vertical Line Features are extracted from a streamed FPV video frame, as the camera is kept approximately leveled through a gimbal system. The features are then matched with Vertical Line Features extracted from a synthetic image of the 3D model. A space resection is performed to provide the EOPs for this frame. The features are tracked in the next frame, followed by an incremental triangulation. The main contribution of this paper lies in automating this process as an FPV video sequence typically consists of thousands of frames. Accuracies of the position and orientation parameters of the video camera and the validation checks of the estimated parameters are presented. Future work includes testing the method in real-time to determine latencies and reliability, and multi-directional field of view of the FPV video camera.