Human Following for Outdoor Mobile Robots Based on Point-Cloud's Appearance Model

GONG Linxi; CAI Yunfei

doi:10.1049/cje.2021.07.017

Volume 30 Issue 6

Nov. 2021

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GONG Linxi and CAI Yunfei, “Human Following for Outdoor Mobile Robots Based on Point-Cloud's Appearance Model,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1087-1095, 2021, doi: 10.1049/cje.2021.07.017

Citation:

GONG Linxi and CAI Yunfei, “Human Following for Outdoor Mobile Robots Based on Point-Cloud's Appearance Model,” Chinese Journal of Electronics, vol. 30, no. 6, pp. 1087-1095, 2021, doi: 10.1049/cje.2021.07.017

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Human Following for Outdoor Mobile Robots Based on Point-Cloud's Appearance Model

doi: 10.1049/cje.2021.07.017

GONG Linxi,
CAI Yunfei^,

School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Funds:

This work is supported by the Fund for Technical Field of the Foundation Strengthening Programme (No.2019-JCJQ-JJ-355).

Received Date: 2020-10-23
Rev Recd Date: 2021-01-28

Available Online: 2021-09-23

Publish Date: 2021-11-05

Abstract

Abstract

In this paper, we propose a point-cloudbased algorithm for human-following robots to detect and follow the target person in a complex outdoor environment. Specifically, we exploit AdaBoost to train a binary classifier in a designed feature space based on sparse point-cloud to distinguish the target person from other objects. Then a particle filter is applied to continuously track the target's position. Motivated by the interference of obstacles in long-distance human-following scenarios, a motion plan algorithm based on vector field histogram is adopted. Experiments are carried out both on the dataset we collected and in real application scenarios. The results show that our algorithm has the ability of real-time target detection and tracking, and is robust to deal with complex situations in outdoor environments.
- Human following,
- Sparse LiDAR pointcloud,
- Target detection,
- Target tracking

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References(15)

References

M.J. Islam, J. Hong and J. Sattar, "Person-following by autonomous robots:A categorical overview", The International Journal of Robotics Research, Vol.38, No.14, pp.1581-1618, 2019.

Y. Xu, Z. Ye, W. Yao, et al., "Classification of LiDAR point clouds using supervoxel-based detrended feature and perception-weighted graphical model", IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol.13, pp.72-88, 2020.

R. Gockley, J. Forlizzi and R. Simmons, "Natural personfollowing behavior for social robots", Proc. of the ACM/IEEE International Conference on Human-robot Interaction, Arlington, Virginia, USA, pp.17-24, 2007.

A.E. Guevara, A. Hoak, J.T. Bernal, et al., "Vision-based selfcontained target following robot using bayesian data fusion", International Symposium on Visual Computing, Las Vegas, USA, pp.846-857, 2016.

Y. Isobe, G. Masuyama and K. Umeda, "Human following with a mobile robot based on combination of disparity and color images", 2014 10th France-Japan/8th Europe-Asia Congress on Mechatronics, Tokyo, Japan, pp.84-88, 2014.

M. Gupta, S. Kumar, L. Behera, et al., "A novel vision-based tracking algorithm for a human-following mobile robot", IEEE Transactions on Systems, Man, and Cybernetics:Systems, Vol.47, No.7, pp.1415-1427, 2016.

Y. Wu, Q. Yang and X. Zhou, "An improved method of optical flow using human body-following wheeled robot", International Journal of Modeling, Simulation, and scientific Computing, Vol.10, No.2, pp.1950003.1-1950003.12, 2019.

B.X. Chen, R. Sahdev and J.K. Tsotsos, "Integrating stereo vision with a CNN tracker for a person-following robot", International Conference on Computer Vision Systems, Shenzhen, China, pp.300-313, 2017.

A. Cosgun, D.A. Florencio and H.I. Christensen, "Autonomous person following for telepresence robots", 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany, pp.4335-4342, 2013.

D. Cha and W. Chung, "Human-leg detection in 3D feature space for a person-following mobile robot using 2D LiDARs", International Journal of Precision Engineering and Manufacturing, Vol.21, pp.1299-1307, 2020.

J. Yuan, S. Zhang, Q. Sun, et al., "Laser-based intersectionaware human following with a mobile robot in indoor environments", IEEE Transactions on Systems, Man, and Cybernetics:Systems, Vol.51, No.1, pp.354-369, 2021.

Y. Sung and W. Chung, "Hierarchical sample-based joint probabilistic data association filter for following human legs using a mobile robot in a cluttered environment", IEEE Transactions on Human-Machine Systems, Vol.46, No.3, pp.340-349, 2016.

K. Kidono, T. Miyasaka, A. Watanabe, et al., "Pedestrian recognition using high-definition LiDAR", 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany, pp.405-410, 2011.

R. Li, L. Zhang and J. Wang, "A control method of unmanned car following under time-varying relative distance and angle", IEEE/CAA Journal of Automatica Sinica, Vol.44, No.1, pp.2031-2040, 2018.

Y. Jiang, Q. Wang, J. Gong, et al., "Research on temporal consistency and robustness in local planning of intelligent vehicles", IEEE/CAA Journal of Automatica Sinica, Vol.41, No.3, pp.518-527, 2015.

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