Browse > Article

A Study of Localization Algorithm of HRI System based on 3D Depth Sensor through Capstone Design  

Lee, Dong Myung (Department of Computer Engineering, Tongmyong University)
Publication Information
Journal of Engineering Education Research / v.19, no.6, 2016 , pp. 49-56 More about this Journal
Abstract
The Human Robot Interface (HRI) based on 3D depth sensor on the docent robot is developed and the localization algorithm based on extended Kalman Filter (EKFLA) are proposed through the capstone design by graduate students in this paper. In addition to this, the performance of the proposed EKFLA is also analyzed. The developed HRI system consists of the route generation and localization algorithm, the user behavior pattern awareness algorithm, the map data generation and building algorithm, the obstacle detection and avoidance algorithm on the robot control modules that control the entire behaviors of the robot. It is confirmed that the improvement ratio of the localization error in EKFLA on the scenarios 1-3 is increased compared with the localization algorithm based on Kalman Filter (KFLA) as 21.96%, 25.81% and 15.03%, respectively.
Keywords
Robot; Extended Kalman Filter; Kinect; Human-Robot Interaction; Localization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. Steinfeld, T. Fong, D. Kaber, M. Lewis, J. Scholtz, A. Schultz, and M. Goodrich(2006), Common Metrics for Human-robot Interaction, In Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-robot Interaction : 33-40.
2 C.Y Choi, E. Myagrmar, D.M Lee, S.R Kwon and H. Choi(2013) Design of HRI System based on 3D Depth Sensor for Awareness of Robot Obstacles and Behaviors, in Proc. KICS Int. Conf. Fall. 2013 : 192-198.
3 A Development of the HRI System based on 3D Depth Sensor for Awareness of Robot Obstacles and Behaviors(2014), Final Report of 2013 IT/SW Creative Research Process (Technologies Development), National IT Industry Promotion Agency(NIPA) : 15-16.
4 ROS (Robot Operating System), http://wiki.ros.org
5 D. Simon(2001), Kalman Filtering, Embedded Systems Programming : 72-79.
6 E.F. Schneider and D. Wildermuth(2004), Using Extended Kalman filter for Relative Localization in a Moving Robot Formation, 4th International Workshop on Robot Motion and Control : 85-90.
7 R. Negenborn(2003), Robot Localization and Kalman Filters: On Finding Your Position in a Noisy World, Utrecht Univ. Master's thesis.
8 G. Welch, and G. Bishop(2001), An Introduction to the Kalman Filter, ACM SIGGRAPH Tutorial.