ISRI > Project > Particle Filtering Framework

  

Particle Filtering Framework

Leader: Seungmin Beak
Contact: Seungmin Beak (smb@skku.edu)


Mailing address:
SungKyunKwan University
Intelligent System Research Center (ISRC)
Nano and Intelligent System Lab
83654 2nd Research building
300 Cheoncheon-dong, Jangan-gu,
Suwon, Gyeonggi-do
440 - 746
Korea
 
Project Description
 
A sequence of images in multiple views rather than a single image from a single view is of great advantage for robust visual recognition and pose estimation of 3D objects in noisy and visually not-so-friendly environments (due to texture, occlusion, illumination, and camera pose). In this project, we have been developing a particle filter based probabilistic method for recognizing an object and estimating its pose based on a sequence of images, where the probability distribution of object pose in 3D space is represented by particles. The particles are updated by consecutive observations in a sequence of images and are converged to a single pose.

* Multiple Evidence and Model Matching in a Sequence of Images by Particle Filtering

 
Personnel
 
 NameTitleEmail Address
 Seungmin BeakResearch Professorsmb@skku.edu
 Junhee LeeMaster Degreeleejunhee@ece.skku.ac.kr
 Jangwon LeeMaster Degreejwlee31@ece.skku.ac.kr
 
Recent publications
 
  • Dependable 3D Recognition and Modeling for Visually Guided Robotic Manipulation and Navigation
  • Sukhan Lee, Jeihun Lee, Seungmin Beak, Dongju Moon and Woong-Myung Kim
    The 5th IARP-IEEE/RAS-EURON Workshop on Technical Challenges for Dependable Robots in Human Environments (IARP07), 2007

    [Download : pdf]

  • Particle Filter Based Robust Recognition and Pose Estimation of 3D Objects in a sequence of images
  • Sukhan Lee, Jeihun Lee, Seungmin Beak and Changhyun Choi
    Lecture Notes In Control and Information Sciences, 2007

    [Download : pdf]

  • Robust Recognition and Pose Estimation of 3D Objects Based on Evidence Fusion in a Sequence of Images
  • Sukhan Lee, Seongsoo Lee, Jeihun Lee, Dongju Moon and Kim Eunyoung
    IEEE International Conference on Robotics and Automation (ICRA), 2007

    [Download : pdf]

  • Recursive Unscented Kalman Filtering based SLAM using a Large Number of Noisy Observations
  • Sukhan Lee, Seongsoo Lee and Dongsung Kim
    International Journal of Control, Automation, and Systems, 2006

    [Download : pdf]



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