Earticle

초록

영어

Localizing humans inside a home or office environment is vital for various service robot applications. This paper presents a novel method to estimate human locations using distributed laser range finders. First, the Kalman filter is employed on the laser data to produce a statistically optimal scan estimate. Next, Scan points in the foreground are then grouped using mean shift clustering algorithm. An area-divided clustering mode is introduced to ensure enough clusters to represent the contour of human. Finally, the centers of humans are estimated from the obtained clusters using incomplete ellipse fitting. Experiments are conducted to prove the robustness and efficiency of the proposed method.

목차

Abstract
 1. Introduction
 2. Data Processing based on the Kalman Filtering
  2.1. Statistic characteristic of laser data
  2.2 Kalman Filtering
 3. Clustering of Detected Edge Points
 4. Cooperative Localization System
  4.1 Distribution of Laser Range Finders
  4.2. Sensor Calibration
  4.3. Human Center Position Extraction
 5. Experimental Results
 6. Conclusion and Future Work
 ACKNOWLEDGEMENTS
 References

키워드

저자

• Peng Duan [ School of Control Science and Engineering, Shandong University, Jinan, 250061, P.R. China ]
• Guohui Tian [ School of Control Science and Engineering, Shandong University, Jinan, 250061, P.R. China ] Corresponding author
• Wei Zhang [ School of Control Science and Engineering, Shandong University, Jinan, 250061, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605