TY - GEN
T1 - Real-time Semantic 3D Reconstruction for High- Touch Surface Recognition for Robotic Disinfection
AU - Qiu, Ri Zhao
AU - Sun, Yixiao
AU - Correia Marques, Joao Marcos
AU - Hauser, Kris
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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
AB - 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
UR - http://www.scopus.com/inward/record.url?scp=85146362251&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146362251&partnerID=8YFLogxK
U2 - 10.1109/IROS47612.2022.9981300
DO - 10.1109/IROS47612.2022.9981300
M3 - Conference contribution
AN - SCOPUS:85146362251
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 9919
EP - 9925
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
Y2 - 23 October 2022 through 27 October 2022
ER -