원문정보
보안공학연구지원센터(IJHIT)
International Journal of Hybrid Information Technology
Vol.7 No.3
2014.05
pp.311-324
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보