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Intelligent Robotics and Applications

Volume 7507 of the series Lecture Notes in Computer Science pp 676-685

A Robotic Pan and Tilt 3-D Target Tracking System by Data Fusion of Vision, Encoder, Accelerometer, and Gyroscope Measurements

  • Tae-Il KimAffiliated withCarnegie Mellon UniversityDepartment of Electrical Engineering and Computer Science/ASRI/ISRC, Seoul National University
  • , Wook BahnAffiliated withCarnegie Mellon UniversityDepartment of Electrical Engineering and Computer Science/ASRI/ISRC, Seoul National University
  • , Chang-Hun LeeAffiliated withCarnegie Mellon UniversityDepartment of Electrical Engineering and Computer Science/ASRI/ISRC, Seoul National University
  • , Tae-Jae LeeAffiliated withCarnegie Mellon UniversityDepartment of Electrical Engineering and Computer Science/ASRI/ISRC, Seoul National University
  • , Byung-Moon JangAffiliated withCarnegie Mellon UniversityDepartment of Electrical Engineering and Computer Science/ASRI/ISRC, Seoul National University
  • , Sang-Hoon LeeAffiliated withCarnegie Mellon UniversityRS Automation
  • , Min-Wug MoonAffiliated withCarnegie Mellon UniversityRS Automation
  • , Dong-Il “Dan” ChoAffiliated withCarnegie Mellon UniversityDepartment of Electrical Engineering and Computer Science/ASRI/ISRC, Seoul National University

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Abstract

This paper presents a vision-tracking system for mobile robots, which travel in a 3-dimentional environment. The developed system controls pan and tilt actuators attached to a camera so that a target is always directly in the line of sight of the camera. This is achieved by using data from robot wheel encoders, a 3-axis accelerometer, a 3-axis gyroscope, pan and tilt motor encoders, and camera. The developed system is a multi-rate sampled data system, where the sampling rate of the camera is different with that of the other sensors. For the accurate estimation of the robot velocity, the developed system detects the slip of robot wheels, by comparing the data from the encoders and the accelerometer. The developed system estimates the target position by using an extended Kalman filter. The experiments are performed to show the tracking performance of the developed system in several motion scenarios, including climbing slopes and slip cases.

Keywords

Vision tracking system Sensor data fusion Kalman filter Slip detection