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Proceedings Paper

3D rigid pose tracking based on new distance function of line segments
Author(s): Langming Zhou; Lihua Xiao; Jiedong Wang; Han Yu
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Paper Abstract

To track and estimate the pose of known rigid objects efficiently in complex environment, we propose a method based on 3D particle filter (PF) with M-estimation optimization. A similarity observation model is put forward according to a new distance function of line segments firstly; secondly, the correspondences between 3D-2D line segments are obtained based on the tracking results of PF. Then, the pose is optimized using M-estimation to minimize the objective function defined according to our new distance metric which integrating the midpoint distance. Finally, the optimized particles are fused into the PF framework according to the importance sampling theory. Experiments indicate that the proposed method can effectively track and accurately estimate the pose of freely moving objects in unconstrained environment. Comparisons on synthetic images demonstrate that our method greatly outperforms the state-of-art method in accuracy and efficiency.

Paper Details

Date Published: 14 August 2019
PDF: 8 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111794L (14 August 2019); doi: 10.1117/12.2540195
Show Author Affiliations
Langming Zhou, Hunan Univ. (China)
Lihua Xiao, Hunan Univ. (China)
Jiedong Wang, The 2nd Surveying and Mapping Institute of Zhejiang Province (China)
Han Yu, The 2nd Surveying and Mapping Institute of Zhejiang Province (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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