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A Robust Real-Time Structure from Motion for Situational Awareness and RSTA

机译:从运动中获取状态感知和RSTA的强大实时结构

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Maintaining real-time situational awareness of military combat vehicles (manned/unmanned) with onboard vision sensors for either autonomous mobility or reconnaissance missions such as moving target indication (MTI) and automatic target recognition (ATR) while the vehicle is on the move has been technically and operationally challenging. In this paper, we investigate and present a practical implementation of a robust real-time structure from motion technique that allows moving robotic vehicles to be able to reconstruct 3D models from observed 2D features with dynamically adjusting motion parameters. We also demonstrate applications that locate and track moving targets within the structured environment built by the SFM and recognize the targets such as vehicles and humans through a hierarchical shape model.
机译:使用车载视觉传感器来保持军事战斗车辆(无人驾驶/无人驾驶)的实时态势感知,以进行自主移动或侦察任务,例如移动时的移动目标指示(MTI)和自动目标识别(ATR)技术上和操作上具有挑战性。在本文中,我们研究并提出了一种来自运动技术的稳健实时结构的实际实现方法,该技术使移动的机器人车辆能够通过动态调整运动参数从观察到的2D特征中重建3D模型。我们还将演示在SFM构建的结构化环境中定位和跟踪移动目标并通过分层形状模型识别目标(例如车辆和人)的应用程序。

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