contributor author | Jin, Shuo | |
contributor author | Dai, Chengkai | |
contributor author | Liu, Yang | |
contributor author | Wang, Charlie C. L. | |
date accessioned | 2017-11-25T07:20:35Z | |
date available | 2017-11-25T07:20:35Z | |
date copyright | 2017/15/6 | |
date issued | 2017 | |
identifier issn | 1530-9827 | |
identifier other | jcise_017_04_041009.pdf | |
identifier uri | http://138.201.223.254:8080/yetl1/handle/yetl/4236546 | |
description abstract | Existing techniques for motion imitation often suffer a certain level of latency due to their computational overhead or a large set of correspondence samples to search. To achieve real-time imitation with small latency, we present a framework in this paper to reconstruct motion on humanoids based on sparsely sampled correspondence. The imitation problem is formulated as finding the projection of a point from the configuration space of a human's poses into the configuration space of a humanoid. An optimal projection is defined as the one that minimizes a back-projected deviation among a group of candidates, which can be determined in a very efficient way. Benefited from this formulation, effective projections can be obtained by using sparsely sampled correspondence, whose generation scheme is also introduced in this paper. Our method is evaluated by applying the human's motion captured by an RGB-depth (RGB-D) sensor to a humanoid in real time. Continuous motion can be realized and used in the example application of teleoperation. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Motion Imitation Based on Sparsely Sampled Correspondence | |
type | Journal Paper | |
journal volume | 17 | |
journal issue | 4 | |
journal title | Journal of Computing and Information Science in Engineering | |
identifier doi | 10.1115/1.4036923 | |
journal fristpage | 41009 | |
journal lastpage | 041009-7 | |
tree | Journal of Computing and Information Science in Engineering:;2017:;volume( 017 ):;issue: 004 | |
contenttype | Fulltext | |