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Augmented reality technology for dayight situational awareness for the dismounted Soldier

机译:增强现实技术可为下岗士兵提供昼/夜状态感知

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This paper describes Applied Research Associates' (ARA) recent advances in Soldier augmented reality (AR) technology. Our AR technology, called ARC4, delivers heads-up situational awareness to the dismounted warfighter, enabling non-line-of-sight team coordination in distributed operations. ARC4 combines compact head tracking sensors with advanced pose estimation algorithms, network management software, and an intuitive AR visualization interface to overlay tactical iconic information accurately on the user's real-world view. The technology supports heads-up navigation, blue-force tracking, target handoff, image sharing, and tagging of features in the environment. It integrates seamlessly with established network protocols (e.g., Cursor-on-Target) and Command and Control software tools (e.g., Nett Warrior, Android Tactical Assault Kit) and interfaces with a wide range of daytime see-through displays and night vision goggles to deliver real-time actionable intelligence, day or night. We describe our pose estimation framework, which fuses inertial data, magnetometer data, GPS, DTED, and digital imagery to provide measurements of the operator's precise orientation. These measurements leverage mountainous terrain horizon geometry, known landmarks, and sun position, enabling ARC4 to achieve significant improvements in accuracy compared to conventional INS/GPS solutions of similar size, weight, and power. We detail current research and development efforts toward helmet-based and handheld AR systems for operational use cases and describe extensions to immersive training applications.
机译:本文介绍了美国应用研究协会(ARA)在士兵增强现实(AR)技术方面的最新进展。我们的AR技术(称为ARC4)可为下架的战斗机提供抬头的态势感知功能,从而在分布式作战中实现非视线团队的协调。 ARC4将紧凑的头部跟踪传感器与高级姿势估计算法,网络管理软件和直观的AR可视化界面相结合,可将战术图标信息准确地叠加在用户的真实世界视图上。该技术支持抬头导航,蓝军追踪,目标越区切换,图像共享以及环境中功能的标记。它与既定的网络协议(例如,目标光标)和命令与控制软件工具(例如,Nett Warrior,Android Tactical Assault Kit)无缝集成,并与各种日间透明显示器和夜视镜相连接。白天或黑夜提供实时可行的情报。我们描述了我们的姿势估计框架,该框架融合了惯性数据,磁力计数据,GPS,DTED和数字图像,以提供对操作员精确方位的测量。这些测量利用了山区的地形视线几何形状,已知的地标和太阳位置,与类似尺寸,重量和功率的常规INS / GPS解决方案相比,使ARC4的准确性得到了显着提高。我们详细介绍了当前针对基于操作用例的头盔式和手持式AR系统的研发成果,并描述了沉浸式训练应用程序的扩展。

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