A method of extracting natural landmarks for mobile robot navigation

被引：0
|
作者：
Niu J. [1 ,2 ]
Bu X. [2 ]
Qian K. [3 ]
机构：
[1] School of Electrical and Electronic Engineering, Changzhou College of Information Technology, Changzhou
[2] School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing
[3] School of Automation, Southeast University, Nanjing
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2019年 / 40卷 / 04期
关键词：
Image clustering; Image segmentation; Landmark detection; Mobile robot; Visual attention;
D O I：
10.11990/jheu.201709095
中图分类号：
学科分类号：
摘要：
Owing to the shortage of artificial landmarks in robot localization and navigation applications, a method of extracting a significant landmark is presented on the basis of frequency domain characteristics. First, this method used the image entropy technique to adaptively select the factor to smooth the image. Then, salience maps of the three-channel color space were obtained by the frequency domain saliency method in the opposite color space. Thus, weighted fusion was conducted. The landmark must be consistent and noise should be reduced. Optimized K-means image clustering method was then used to obtain masked final landmarks. The natural landmarks available for robot navigation applications were selected. The experiments show that the pixels extracted by the visual feature reach an average detection rate of 80%. Furthermore, the proposed method has high reproducibility indoors compared with direct matching of characteristic operators. Finally, practical robot navigation based on the natural landmark validates the effectiveness of the method. © 2019, Editorial Department of Journal of HEU. All right reserved.
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页码：844 / 850
页数：6
相关论文
共 16 条
  • [1] Kunii Y., Kovacs G., Hoshi N., Mobile robot navigation in natural environments using robust object tracking, 2017 IEEE 26th International Symposium on Industrial Electronics, pp. 1747-1752, (2017)
  • [2] Pfrunder A., Borges P.V.K., Romero A.R., Et al., Real-time autonomous ground vehicle navigation in heterogeneous environments using a 3D LIDAR, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2601-2608, (2017)
  • [3] Beinhofer M., Muller J., Burgard W., Effective landmark placement for accurate and reliable mobile robot navigation, Robotics and Autonomous Systems, 61, 10, pp. 1060-1069, (2013)
  • [4] Choset H., Nagatani K., Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization, IEEE Transactions on Robotics and Automation, 17, 2, pp. 125-137, (2002)
  • [5] Zeidan B., Dasgupta S., Worgotter F., Et al., Adaptive landmark-based navigation system using learning techniques, International Conference on Simulation of Adaptive Behavior, 8575, pp. 121-131, (2014)
  • [6] Luke R.H., Keller J.M., Skubic M., Et al., Acquiring and maintaining abstract landmark chunks for cognitive robot navigation, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2566-2571, (2005)
  • [7] Wu P., Xu H., Li W., Et al., Multi-scale Harris-corner detection algorithm based on region detection, Journal of Harbin Engineering University, 37, 7, pp. 969-973, (2016)
  • [8] Zhao C., Zhou Y., Wei Y., Et al., Visual saliency landmark detection algorithm based on DNN feature extraction, 201635th Chinese Control Conference, pp. 5551-5556, (2016)
  • [9] Achanta R., Hemami S., Estrada F., Et al., Frequency-tuned salient region detection, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1597-1604, (2009)
  • [10] Xu L., Lu C., Xu Y., Et al., Image smoothing via L<sub>0</sub> gradient minimization, ACM Transactions on Graphics, 30, 6, pp. 1-12, (2011)
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