Abstract
Reliable human following is one of the key capabilities of service and personal assisting robots. This paper presents a novel person tracking and following approach for autonomous mobile robots that are equipped with a 2D laser rangefinder (LRF) and a monocular camera. The proposed method does not impose restrictions on a person’s clothes, does not require a head or an upper body to be within a camera field of view and is suitable for low height indoor robots as well. The algorithm is based on a metric that takes into an account parameters obtained directly from LRF and monocular camera data. The algorithm was implemented and tested in the Gazebo simulator. Next, it was integrated into a control system of the TIAGo Base mobile robot and successfully validated in university environment experiments with real people. In addition, this paper proposes a new criterion of algorithm performance estimation, which is a function of false positives number and traveled distances by a person and by a robot. Further this criterion is used to compare performance of the proposed method with the Multiple Instance Learning (MIL) tracker in simulated and in real world environments.
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References
PMB-2 technical specifications. http://pal-robotics.com/wp-content/uploads/2016/07/PMB-2-Datasheet.pdf
Tiago base. http://wiki.ros.org/Robots/PMB-2
Abbyasov, B., Lavrenov, R., Zakiev, A., Yakovlev, K., Svinin, M., Magid, E.: Automatic tool for gazebo world construction: from a grayscale image to a 3D solid model. In: International Conference on Robotics and Automation (ICRA), pp. 7226–7232 (2020)
Arras, K.O., et al.: Range-based people detection and tracking for socially enabled service robots. In: Prassler, E., et al. (eds.) Towards Service Robots for Everyday Environments, pp. 235–280. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-25116-0_18
Babenko, B., Yang, M., Belongie, S.: Visual tracking with online multiple instance learning. In: Conference on Computer Vision and Pattern Recognition, pp. 983–990. IEEE (2009)
Bereznikov, D., Zakiev, A.: Network failure detection and autonomous return for PMB-2 mobile robot. In: International Conference on Artificial Life and Robotics (ICAROB 2020), pp. 444–447 (2020)
Bohannon, R.: Comfortable and maximum walking speed of adults aged 20–79 years: Reference values and determinants. Age Ageing 26(1), 15–19 (1997)
Carballo, A., Ohya, A., Yuta, S.: Reliable people detection using range and intensity data from multiple layers of laser range finders on a mobile robot. Int. J. Soc. Robot. 3, 167–186 (2011)
Chebotareva, E., Hsia, K.H., Yakovlev, K., Magid, E.: Laser rangefinder and monocular camera data fusion for human-following algorithm by PMB-2 mobile robot in simulated Gazebo environment. Smart Innovation, Syst. Technol. 187 (2020)
Chen, B.X., Sahdev, R., Tsotsos, J.K.: Integrating stereo vision with a CNN tracker for a person-following robot. In: Liu, M., Chen, H., Vincze, M. (eds.) ICVS 2017. LNCS, vol. 10528, pp. 300–313. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-68345-4_27
Franěk, M.: Environmental factors influencing pedestrian walking speed. Percept. Mot. Skills 116(3), 992–1019 (2013)
Guerrero-Higueras, Á.M., et al.: Tracking people in a mobile robot from 2D LIDAR scans using full convolutional neural networks for security in cluttered environments. Front. Neurorobot. 12 (2018)
Islam, M.J., Hong, J., Sattar, J.: Person-following by autonomous robots: a categorical overview. The Int. J. Robot. Res. 38(14), 1581–1618 (2019)
Islam, M.M., Lam, A., Fukuda, H., Kobayashi, Y., Kuno, Y.: An intelligent shopping support robot: understanding shopping behavior from 2D skeleton data using GRU network. ROBOMECH J. 6(1), 1–10 (2019). https://doi.org/10.1186/s40648-019-0150-1
Jiang, S., Li, L., Hang, M., Kuc, T.: An adaptive 2D tracking approach for person following robot. In: International Symposium on Computer Science and Intelligent Controls, pp. 147–151 (2017)
Kawarazaki, N., et al.: Development of human following mobile robot system using laser range scanner. Procedia Comput. Sci. 76, 455–460 (2015)
Kim, H., et al.: Sensor fusion-based human tracking using particle filter and data mapping analysis in in/outdoor environment. In: International Conference on Ubiquitous Robots and Ambient Intelligence, pp. 741–744 (2013)
Koide, K., et al.: Monocular person tracking and identification with on-line deep feature selection for person following robots. Robot. Auton. Syst. 124 (2020)
Kristou, M., et al.: Target person identification and following based on omnidirectional camera and LRF data fusion. In: International Conference on Robot & Human Interactive Communication, pp. 419–424. IEEE (2011)
Lang, A., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: fast encoders for object detection from point clouds. In: Conference on Computer Vision and Pattern Recognition, pp. 12689–12697 (2019)
Lavrenov, R., Matsuno, F., Magid, E.: Modified spline-based navigation: guaranteed safety for obstacle avoidance. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2017. LNCS (LNAI), vol. 10459, pp. 123–133. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66471-2_14
Lavrenov, R.O., Magid, E.A., Matsuno, F., Svinin, M.M., Suthakorn, J.: Development and implementation of spline-based path planning algorithm in ROS/gazebo environment. Trudy SPIIRAN 18(1), 57–84 (2019)
Lee, B.J., et al.: Robust human following by deep Bayesian trajectory prediction for home service robots. In: International Conference on Robotics and Automation, pp. 7189–7195. IEEE (2018)
Leigh, A., et al.: Person tracking and following with 2D laser scanners. In: International Conference on Robotics and Automation, pp. 726–733. IEEE (2015)
Magid, E., Lavrenov, R., Khasianov, A.: Modified spline-based path planning for autonomous ground vehicle. ICINCO 2, 132–141 (2017)
Moskvin, I., Lavrenov, R.: Modeling tracks and controller for servosila engineer robot. In: Ronzhin, A., Shishlakov, V. (eds.) Proceedings of 14th International Conference on Electromechanics and Robotics “Zavalishin’s Readings”. SIST, vol. 154, pp. 411–422. Springer, Singapore (2020). https://doi.org/10.1007/978-981-13-9267-2_33
Nakamori, Y., Hiroi, Y., Ito, A.: Multiple player detection and tracking method using a laser range finder for a robot that plays with human. ROBOMECH Journal 5(1), 1–15 (2018). https://doi.org/10.1186/s40648-018-0122-x
Orita, Y., Fukao, T.: Robust human tracking of a crawler robot. J. Robot. Mechatron. 31(2), 194–202 (2019)
Pages, J., Marchionni, L., Ferro, F.: Tiago: the modular robot that adapts to different research needs. In: International Workshop on Robot Modularity, IROS (2016)
Ren, Q., et al.: Real-time target tracking system for person-following robot. In: Chinese Control Conference, pp. 6160–6165 (2016)
Ronzhin, A., Saveliev, A., Basov, O., Solyonyj, S.: Conceptual model of cyberphysical environment based on collaborative work of distributed means and mobile robots. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds.) ICR 2016. LNCS (LNAI), vol. 9812, pp. 32–39. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43955-6_5
Safin, R., Lavrenov, R., Tsoy, T., Svinin, M., Magid, E.: Real-time video server implementation for a mobile robot. In: 11th International Conference on Developments in eSystems Engineering (DeSE), pp. 180–185. IEEE (2018)
Sato, Y., et al.: A maneuverable robotic wheelchair able to move adaptively with a caregiver by considering the situation. In: International Conference on Robot & Human Interactive Communication, pp. 282–287. IEEE (2013)
Simakov, N., Lavrenov, R., Zakiev, A., Safin, R., Martínez-García, E.A.: Modeling USAR maps for the collection of information on the state of the environment. In: 2019 12th International Conference on Developments in eSystems Engineering (DeSE), pp. 918–923. IEEE (2019)
Sung, Y., Chung, W.: Hierarchical sample-based joint probabilistic data association filter for following human legs using a mobile robot in a cluttered environment. IEEE Trans. Hum. Mach. Syst. 46(3), 340–349 (2015)
Yan, Y., Mao, Y., Li, B.: SECOND: sparsely embedded convolutional detection. Sensors 18, 3337 (2018)
Acknowledgements
The reported study was funded by the Russian Foundation for Basic Research (RFBR) according to the research project No. 19–58-70002. Special thanks to PAL Robotics for their kind professional support with TIAGo Base robot software and hardware related issues.
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Chebotareva, E., Safin, R., Hsia, KH., Carballo, A., Magid, E. (2020). Person-Following Algorithm Based on Laser Range Finder and Monocular Camera Data Fusion for a Wheeled Autonomous Mobile Robot. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2020. Lecture Notes in Computer Science(), vol 12336. Springer, Cham. https://doi.org/10.1007/978-3-030-60337-3_3
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