Real-time Semantic 3D Reconstruction for High- Touch Surface Recognition for Robotic Disinfection
Real-time Semantic 3D Reconstruction for High- Touch Surface Recognition for Robotic Disinfection
复制标题
用于机器人消毒的高接触表面识别的实时语义 3D 重建
DOI：
10.1109/iros47612.2022.9981300
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Kris K. Hauser
中科院分区：
文献类型：
--
作者：
Ri;Yixiao Sun;J. M. C. Marques;Kris K. Hauser
消毒机器人在促进公共卫生和减少医院获得性感染方面具有应用，并且由于COVID-19大流行而引起了相当大的兴趣。为了快速消毒房间，可以使用运动规划来在房间表面的重建3D地图上规划机器人消毒轨迹。然而，现有的方法丢弃房间的语义信息，因此，需要很长的时间来执行彻底的消毒。另一方面，人类清洁工通过优先清洁高接触表面来更有效地消毒房间。为了解决这个问题，我们提出了一种新的基于GPU的体积语义TSDF（截断有符号距离函数）集成系统的语义三维重建。我们的系统在消费级GPU上以每秒约50帧的速度生成3D重建，将高触摸表面与非高触摸表面区分开来，这比现有的基于CPU的TSDF语义重建方法快约5倍。此外，我们扩展了紫外线消毒运动规划算法，将语义感知优化覆盖的消毒轨迹。实验表明，我们的语义感知规划优于几何规划，在相同的时间预算下，消毒高达20%以上的高触摸表面。此外，我们的语义重建管道的实时性，使未来的工作同步消毒和映射。代码可在：https://github.com/uiuc-iml/RA-SLAM
Disinfection robots have applications in promoting public health and reducing hospital acquired infections and have drawn considerable interest due to the COVID-19 pan-demic. To disinfect a room quickly, motion planning can be used to plan robot disinfection trajectories on a reconstructed 3D map of the room's surfaces. However, existing approaches discard semantic information of the room and, thus, take a long time to perform thorough disinfection. Human cleaners, on the other hand, disinfect rooms more efficiently by prioritizing the cleaning of high-touch surfaces. To address this gap, we present a novel GPU-based volumetric semantic TSDF (Truncated Signed Distance Function) integration system for semantic 3D reconstruction. Our system produces 3D reconstructions that distinguish high-touch surfaces from non-high-touch surfaces at approximately 50 frames per second on a consumer-grade GPU, which is approximately 5 times faster than existing CPU-based TSDF semantic reconstruction methods. In addition, we extend a UV disinfection motion planning algorithm to incorporate semantic awareness for optimizing coverage of disinfection tra-jectories. Experiments show that our semantic-aware planning outperforms geometry-only planning by disinfecting up to 20% more high-touch surfaces under the same time budget. Further, the real-time nature of our semantic reconstruction pipeline enables future work on simultaneous disinfection and mapping. Code is available at: https://github.com/uiuc-iml/RA-SLAM
优化紫外线表面消毒的覆盖范围规划
DOI：
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发表时间：
2021
期刊：
IEEE International Conference on Robotics and Automation
影响因子：
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作者：
Marques, Joao;Ramalingam, Ramya;Pan, Zherong;Hauser, Kris
通讯作者：
Hauser, Kris
影响因子：
6.2
作者：
Niessner, Matthias;Zollhoefer, Michael;Stamminger, Marc
通讯作者：
Stamminger, Marc