Abstract：A target recognition and registration algorithm based on domain knowledge in 3-dimensional dynamic scene is proposed to enhance the sensory immersion of the teleoperation system and make the operator better integrate into the remote working environment. Firstly, a domain knowledge database containing multi-view point cloud features and assembly constraints is constructed by offline parsing and segmentation of virtual prototype CAD (computer aided design) models. Then, the CVFH (clustered viewpoint feature histogram) and FPFH (fast point feature histogram) features are computed by dynamically collecting scene point clouds, and the multi-view point cloud features contained in domain knowledge database and the CVFH features are compared to implement target recognition. The target pose is obtained using the FPFH features through two-step registration. Finally, the accurate registration and real-time push of guidance information driven by the changes of working state of the teleoperation robot are implemented using the assembly constraint knowledge database. The experimental results show that the algorithm can not only effectively guide the remote robot to complete the maintenance operation, but also improve the precision and efficiency of teleoperation.
[1] Kim W S, Bejczy A K. Demonstration of a high-fidelity predictive/preview display technique for telerobotic servicing inspace[J]. IEEE Transactions on Robotics and Automation, 1993, 9(5):698-704.
[2] Kelly A, Chan N, Herman H, et al. Real-time photorealistic virtualized reality interface for remote mobile robot control[J]. International Journal of Robotics Research, 2011, 30(3):384-404.
[3] 徐效农,宋爱国,朱澄澄,等.基于环境建模与修正的视觉/力觉辅助遥操作系统[J].载人航天,2016,22(1):55-61.Xu X N, Song A G, Zhu C C, et al. Visual-haptic aid teleoperation system based on environment modeling and updating[J]. Manned Spaceflight, 2016, 22(1):55-61.
[4] Ni D J, Song A G, Xu X N, et al. 3D-point-cloud registration and real-world dynamic modelling-based virtual environmentbuilding method for teleoperation[J]. Robotica, 2017, 35(10):1958-1974.
[5] 丑武胜,孟偲,王田苗.基于多通道增强现实的机器人遥操作技术研究[J].高技术通讯,2004,14(10):49-52.Chou W S, Meng C, Wang T M. Study on teleoperation based on multimodal augmented reality[J]. High Technology Letters, 2004, 14(10):49-52.
[6] Aldoma A, Vincze M, Blodow N, et al. CAD-model recognition and 6DOF pose estimation using 3D cues[C]//IEEE International Conference on Computer Vision. Piscataway, USA:IEEE, 2011:585-592.
[7] Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration[C]//IEEE International Conference on Robotics and Automation. Piscataway, USA:IEEE, 2009:3212-3217.
[8] Aldoma A, Marton Z C, Tombari F, et al. Tutorial:Point cloud library:Three-dimensional object recognition and 6 DOF pose estimation[J]. IEEE Robotics & Automation Magazine, 2012, 19(3):80-91.
