Abstract
The transfer from the utilization of simple robots for specifically predefined tasks to the integration of generalized autonomous systems poses a number of challenges for the collaboration between humans and robots. These include the independent orientation of robots in unknown environments and the intuitive interaction with human cooperation partners. We present a robust human-robot interaction (HRI) system that proactively searches for interaction partners and follows them in unknown real environments. For this purpose, an algorithm for simultaneous localization and mapping of the environment is integrated along with a dynamic system for determination of the partner’s willingness and the tracking of the partner’s localization. Interruptions of interactions are recovered by a separate recovery mode that is able to identify prior collaboration partners.
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References
Awais, M., Henrich, D.: Human-robot collaboration by intention recognition using probabilistic state machines. In: 19th International Workshop on Robotics in Alpe-Adria-Danube Region (RAAD 2010). pp. 75–80, June 2010. https://doi.org/10.1109/RAAD.2010.5524605
Bolme, D.S., Beveridge, J.R., Draper, B.A., Lui, Y.M.: Visual object tracking using adaptive correlation filters. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2544–2550 (2010)
Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools (2000)
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: real-time single camera slam. IEEE Trans Pattern Anal. Mach. Intell. 29, 1052–1067 (2007)
Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. CoRR abs/1607.02565 (2016). http://arxiv.org/abs/1607.02565
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2015)
Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867–1874 (2014)
Kerl, C., Sturm, J., Cremers, D.: Dense visual slam for RGB-D cameras. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2100–2106, November 2013. https://doi.org/10.1109/IROS.2013.6696650
Li, S., Zhang, L., Diao, X.: Deep-learning-based human intention prediction using RGB images and optical flow. J. Intell. Robot. Syst. 97(1), 95–107 (2019). https://doi.org/10.1007/s10846-019-01049-3
Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM 2.0: an improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI), IJCAI, Acapulco, Mexico (2003)
Mur-Artal, R., Montiel, J.M.M., Tardós, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. CoRR abs/1502.00956 (2015)
Mur-Artal, R., Tardos, J.D.: ORB-SLAM2: an open-source slam system for monocular, stereo, and RGB-D cameras. IEEE Trans. Rob. 33(5), 1255–1262 (2017). https://doi.org/10.1109/tro.2017.2705103
Newcombe, R.A., Lovegrove, S., Davison, A.J.: DTAM: dense tracking and mapping in real-time. In: Metaxas, D.N., Quan, L., Sanfeliu, A., Gool, L.J.V. (eds.) ICCV, pp. 2320–2327. IEEE (2011)
Pire, T., Fischer, T., Castro, G., De Cristóforis, P., Civera, J., Berlles, J.: S-PTAM: stereo parallel tracking and mapping. Robot. Auton. Syst. 93, 27–42 (2017). https://doi.org/10.1016/j.robot.2017.03.019
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). http://arxiv.org/abs/1804.02767. cite arxiv:1804.02767Comment. Technical report
Svenstrup, M., Tranberg, S., Andersen, H.J., Bak, T.: Pose estimation and adaptive robot behaviour for human-robot interaction. In: 2009 IEEE International Conference on Robotics and Automation, pp. 3571–3576, May 2009. https://doi.org/10.1109/ROBOT.2009.5152690
Tistarelli, M., Grosso, E.: Human face analysis: from identity to emotion and intention recognition. In: Kumar, A., Zhang, D. (eds.) ICEB 2010. LNCS, vol. 6005, pp. 76–88. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12595-9_11
Wolf, L., Hassner, T., Taigman, Y.: Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1978–1990 (2011). https://doi.org/10.1109/TPAMI.2010.230
This work is funded by the Federal Ministry of Education and Research (BMBF) (RoboAssist no. 03ZZ0448G-L) within 3Dsensation alliance.
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Hempel, T., Al-Hamadi, A. (2020). SLAM-Based Multistate Tracking System for Mobile Human-Robot Interaction. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_32
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