STARC: See-Through-Wall Augmented Reality Framework for Human-Robot Collaboration in Emergency Response
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866918144191234048 |
|---|---|
| author | Yuan, Shenghai Guo, Weixiang Hu, Tianxin Yang, Yu Chen, Jinyu Qian, Rui Liu, Zhongyuan Xie, Lihua |
| author_facet | Yuan, Shenghai Guo, Weixiang Hu, Tianxin Yang, Yu Chen, Jinyu Qian, Rui Liu, Zhongyuan Xie, Lihua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15507 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STARC: See-Through-Wall Augmented Reality Framework for Human-Robot Collaboration in Emergency Response Yuan, Shenghai Guo, Weixiang Hu, Tianxin Yang, Yu Chen, Jinyu Qian, Rui Liu, Zhongyuan Xie, Lihua Robotics 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. |
| title | STARC: See-Through-Wall Augmented Reality Framework for Human-Robot Collaboration in Emergency Response |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.15507 |