Simultaneous Segmentation, Estimation and Analysis of Articulated Motion from Dense Point Cloud Sequence

Title
Simultaneous Segmentation, Estimation and Analysis of Articulated Motion from Dense Point Cloud Sequence
Authors
안상철임화섭김영지김아영
Issue Date
2016-10
Publisher
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2016
Citation
-8
Abstract
In this paper, we present a unified approach for Expectation Maximization (EM) based motion segmentation, estimation and analysis from dense point cloud data. When identifying an underlying motion, literature mainly focuses on three related topics: motion segmentation, estimation and analysis. These topics are, however, mostly considered separately while integrated approaches are rare. Our approach specifically focuses on analyzing articulated motion from dense point cloud data by simultaneously solving for three topics using an integrated approach. No prior knowledge, such as background regions, number of segments and correspondence, is required since two iterations in this algorithm allow us to seamlessly accomplish integration of the three tasks. The first iteration of the algorithm is performed between segmentation and estimation, followed by the second iteration between motion estimation and analysis. For the first iteration, we propose EM based subspace clustering algorithm. For the second iteration, we simply fuse the motion analysis method from [1] into an iterative motion estimation algorithm. As a result, we can extract label, correspondence and motion of moving objects simultaneously from dense point cloud sequence. In experiment, we validate the performance of the proposed method on both synthetic and real world data.
URI
http://pubs.kist.re.kr/handle/201004/64808
Appears in Collections:
KIST Publication > Conference Paper
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