Robust 3D visual tracking using particle filtering on the SE(3) group

Changhyun Choi, Henrik I. Christensen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

In this paper, we present a 3D model-based object tracking approach using edge and keypoint features in a particle filtering framework. Edge points provide 1D information for pose estimation and it is natural to consider multiple hypotheses. Recently, particle filtering based approaches have been proposed to integrate multiple hypotheses and have shown good performance, but most of the work has made an assumption that an initial pose is given. To remove this assumption, we employ keypoint features for initialization of the filter. Given 2D-3D keypoint correspondences, we choose a set of minimum correspondences to calculate a set of possible pose hypotheses. Based on the inlier ratio of correspondences, the set of poses are drawn to initialize particles. For better performance, we employ an autoregressive state dynamics and apply it to a coordinate-invariant particle filter on the SE(3) group. Based on the number of effective particles calculated during tracking, the proposed system re-initializes particles when the tracked object goes out of sight or is occluded. The robustness and accuracy of our approach is demonstrated via comparative experiments.

Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Robotics and Automation, ICRA 2011
Pages4384-4390
Number of pages7
DOIs
StatePublished - Dec 1 2011
Event2011 IEEE International Conference on Robotics and Automation, ICRA 2011 - Shanghai, China
Duration: May 9 2011May 13 2011

Other

Other2011 IEEE International Conference on Robotics and Automation, ICRA 2011
CountryChina
CityShanghai
Period5/9/115/13/11

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