让救援人员透视墙体，实时看到隐藏的被困者和危险
STARC: See-Through-Wall Augmented Reality Framework for Human-Robot Collaboration in Emergency Response
- 用机器人建图+救援员手持激光雷达融合定位
- 实现毫米级精度的跨设备点云对齐与低延迟渲染
- 适合消防、灾难救援等高风险场景使用
在应急救援任务中，救援人员需穿越充满障碍物的室内环境，视线被遮挡导致潜在危险和受困者难以发现。本文提出STARC框架，一种用于人机协同的透视式增强现实系统，通过移动机器人进行大范围三维建图与人体检测，并结合佩戴于救援人员头盔或手持的激光雷达，利用相对位姿估计将个人传感器数据注册到机器人全局地图中。该跨激光雷达对齐技术实现了第一视角下对检测到的人体及其点云的稳定投影，在增强现实中以低延迟呈现。系统可实时展示隐藏的受困者与危险源，显著提升态势感知能力并降低操作风险。仿真、实验室及实战演练测试验证了姿态对齐的鲁棒性、检测可靠性以及叠加效果的稳定性，证明其在消防、灾害救援等安全关键任务中的应用潜力。代码与设计将在录用后开源。
原文摘要 · Abstract (English)
In emergency response missions, first responders must navigate cluttered indoor environments where occlusions block direct line-of-sight, concealing both life-threatening hazards and victims in need of rescue. We present STARC, a see-through AR framework for human-robot collaboration that fuses mobile-robot mapping with responder-mounted LiDAR sensing. A ground robot running LiDAR-inertial odometry performs large-area exploration and 3D human detection, while helmet- or handheld-mounted LiDAR on the responder is registered to the robot's global map via relative pose estimation. This cross-LiDAR alignment enables consistent first-person projection of detected humans and their point clouds - rendered in AR with low latency - into the responder's view. By providing real-time visualization of hidden occupants and hazards, STARC enhances situational awareness and reduces operator risk. Experiments in simulation, lab setups, and tactical field trials confirm robust pose alignment, reliable detections, and stable overlays, underscoring the potential of our system for fire-fighting, disaster relief, and other safety-critical operations. Code and design will be open-sourced upon acceptance.
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