Abstract
We present a time-dependent probabilistic map able to model and predict flow patterns of people in indoor environments. The proposed representation models the likelihood of motion direction on a grid-based map by a set of harmonic functions, which efficiently capture long-term (minutes to weeks) variations of crowd movements over time. The evaluation, performed on data from two real environments, shows that the proposed model enables prediction of human movement patterns in the future. Potential applications include human-aware motion planning, improving the efficiency and safety of robot navigation.
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References
Palmieri, L., Kucner, T.P., Magnusson, M., Lilienthal, A.J., Arras, K.O.: Kinodynamic motion planning on Gaussian mixture fields. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 6176–6181. IEEE (2017)
Krajník, T., Fentanes, J.P., Santos, J., Duckett, T.: FreMEn: frequency map enhancement for long-term mobile robot autonomy in changing environments. IEEE Trans. Robot. (2017)
Brscic, D., Kanda, T., Ikeda, T., Miyashita, T.: Person position and body direction tracking in large public spaces using 3D range sensors. IEEE Trans. Hum.-Mach. Syst. 43(6), 522–534 (2013)
Arbuckle, D., Howard, A., Mataric, M.: Temporal occupancy grids: a method for classifying the spatio-temporal properties of the environment. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2002)
Mitsou, N., Tzafestas, C.: Temporal occupancy grid for mobile robot dynamic environment mapping. In: Mediterranean Conference on Control Automation (2007)
Sun, L., Yan, Z., Molina, S., Hanheide, M., Duckett, T.: 3DOF pedestrian trajectory prediction learned from long-term autonomous mobile robot deployment data. In: IEEE International Conference on Robotics and Automation (2018)
Rudenko, A., Palmieri, L., Arras, O.: Joint long-term prediction of human motion using a planning-based social force approach. In: IEEE International Conference on Robotics and Automation (ICRA) (2018)
Bennewitz, M., Burgard, W., Cielniak, G., Thrun, S.: Learning motion patterns of people for compliant robot motion. IJRR 24(1), 31–48 (2005)
Wang, Z., Ambrus, R., Jensfelt, P., Folkesson, J.: Modeling motion patterns of dynamic objects by IOHMM. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 1832–1838. IEEE (2014)
Kucner, T., Saarinen, J., Magnusson, M., Lilienthal, A.J.: Conditional transition maps learning motion patterns in dynamics environments. In: IEEE International Conference on Intelligent Robots and Systems (2013)
Saarinen, J., Andreasson, H., Lilienthal, A.J.: Independent Markov chain occupancy grid maps for representation of dynamic environment. In: IEEE Intelligent Robots and Systems, pp. 3489–3495. IEEE (2012)
Dayoub, F., Cielniak, G., Duckett, T.: Long-term experiments with an adaptive spherical view representation for navigation in changing environments. Robot. Auton. Syst. 59, 285–295 (2011)
Rosen, D.M., Mason, J., Leonard, J.J.: Towards lifelong feature-based mapping in semi-static environments. In: International Conference on Robotics and Automation (ICRA), pp. 1063–1070. IEEE, May 2016
Tipaldi, G.D., Meyer-Delius, D., Burgard, W.: Lifelong localization in changing environments. IJRR 32, 1662–1678 (2013)
Fentanes, J.P., Lacerda, B., Krajník, T., Hawes, N., Hanheide, M.: Now or later? Predicting and maximising success of navigation actions from long-term experience. In: International Conference on Robotics and Automation, pp. 1112–1117, May 2015
Santos, J.M., Krajnik, T., Fentanes, J.P., Duckett, T.: Lifelong information-driven exploration to complete and refine 4D spatio-temporal maps. Robot. Autom. Lett. 1, 684–691 (2016)
Jovan, F., et al.: A poisson-spectral model for modelling temporal patterns in human data observed by a robot. In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4013–4018, October 2016
Yan, Z., Duckett, T., Bellotto, N., et al.: Online learning for human classification in 3D LiDAR-based tracking. In: International Conference on Intelligent Robots and Systems (IROS) (2017)
Molina, S., Cielniak, G., Krajnik, T., Duckett, T.: Modelling and predicting rhythmic flow patterns in dynamics environments. In: Robotics and Autonomous Systems: Robots Working for and Among Us (2017)
Acknowledgement
This work has been supported within H2020-ICT by the EC under grant number 732737 (ILIAD), by CSF grant no. 17-27006Y and OP VVV Research Center for Informatics no. CZ.02.1.01/0.0/0.0/16_019/0000765.
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Molina, S., Cielniak, G., Krajník, T., Duckett, T. (2018). Modelling and Predicting Rhythmic Flow Patterns in Dynamic Environments. In: Giuliani, M., Assaf, T., Giannaccini, M. (eds) Towards Autonomous Robotic Systems. TAROS 2018. Lecture Notes in Computer Science(), vol 10965. Springer, Cham. https://doi.org/10.1007/978-3-319-96728-8_12
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DOI: https://doi.org/10.1007/978-3-319-96728-8_12
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