Xu Cheng

Mr. Xu Cheng


Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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19 March 2019 in Sensors
Verified
Journal Article
Open access
PDF file
LHLiya Han
Liya Han
LZLi Zhongwei
Li Zhongwei
KZKai Zhong
Kai Zhong
XCXu Cheng
Xu Cheng
HLHua Luo
Hua Luo
Yusheng ShiYusheng Shi
Yusheng Shi
Phase-shifting profilometry, especially employing the multi-frequency phase-shifting method, is increasingly used for in situ 3D metrology and for the inspection of industrial parts. However, environmental vibrations cause fatal measurement errors and are inevitable in such applications. To this end, an effective and fast vibration detection and motion compensation method for multi-frequency phase-shifting-based 3D sensors is presented. The proposed method quantitatively indicates the strength of the vibration and compensates for the motion error by revising the wrapped phase without accessing neighboring pixels. Different vibration intensities were simulated using an industrial robot moving to test the feasibility of the method. According to experiments, this method is valid and capable for 3D inspection systems affected by inevitable vibrations.

6 February 2019 in Sensors
Verified
Journal Article
Open access
PDF file
XCXu Cheng
Xu Cheng
XLXingjian Liu
Xingjian Liu
LZLi Zhongwei
Li Zhongwei
KZKai Zhong
Kai Zhong
LHLiya Han
Liya Han
Yusheng ShiYusheng Shi
Yusheng Shi
This paper presents a high-accuracy method for globally consistent surface reconstruction using a single fringe projection profilometry (FPP) sensor. To solve the accumulated sensor pose estimation error problem encountered in a long scanning trajectory, we first present a novel 3D registration method which fuses both dense geometric and curvature consistency constraints to improve the accuracy of relative sensor pose estimation. Then we perform global sensor pose optimization by modeling the surface consistency information as a pre-computed covariance matrix and formulating the multi-view point cloud registration problem in a pose graph optimization framework. Experiments on reconstructing a 1300 mm × 400 mm workpiece with a FPP sensor is performed, verifying that our method can substantially reduce the accumulated error and achieve industrial-level surface model reconstruction without any external positional assistance but only using a single FPP sensor.