Sensor Fusion Based Human Detection and Tracking System for Human-Robot Interaction
Date Issued
2012
Date
2012
Author(s)
Ong, Kai-Siang
Abstract
Service robot has received enormous attention with rapid development of high technology in recent years, and it is endowed with the capabilities of interacting with people and performing human-robot interaction (HRI). For this purpose, the Sampling Importance Resampling (SIR) particle filter is adopted to implement the laser and visual based human tracking system when dealing with human-robot interaction (HRI) in real world environment. The sequence of images and the geometric information from measurements are provided by the vision sensor and the laser range finder (LRF), respectively.
We construct a sensor fusion based system to integrate the information from both sensors by using a data association approach – Covariance Intersection (CI). It will be used to increase the robustness and reliability of human in the real world environment. In this thesis, we propose a Behavior System for analyze human features and classify the behavior by the crucial information from sensor fusion based system. The system is used to infer the human behavioral intentions, and also allow the robot to perform more natural and intelligent interaction. We apply a spatial model based on proxemics rules to our robot, and design a behavioral intention inference strategy. Furthermore, the robot will make the corresponding reaction in accordance with the identified behavioral intention. This work concludes with several experimental results with a robot in indoor environment, and promising performance has been observed.
Subjects
human behavior intention inference
sensor fusion based system
Covariance Intersection(CI)
robot reaction decision
human-robot interaction
Type
thesis
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