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A field measurement method for large objects based on a multi-view stereo vision system.pdf
Sensors and Actuators A 234 (2015) 120–132
Contents lists available at ScienceDirect
Sensors and Actuators A: Physical
journal homepage: /locate/sna
A ?eld measurement method for large objects based on a multi-view stereo vision system
Zhenyuan Jia, Lingli Wang, Wei Liu ?, Jinghao Yang, Yang Liu, Chaonan Fan, Kai Zhao
Key Laboratory for Precision and Non-Traditional Machining Technology of the Ministry of Education, Dalian University of Technology, Dalian 116024, China
article info
Article history: Received 23 March 2015 Received in revised form 30 August 2015 Accepted 30 August 2015 Available online 2 September 2015
Keywords: Field measurement Multi-view stereo vision system Laser-aided pattern High-precision extraction method
abstract
Field measurement of large objects is important in many ?elds of study. In this paper, an effective ?eld measurement method for large objects based on a multi-view stereo vision system is proposed. First, to reconstruct the dimensional features of large objects, partially saturated laser stripes are projected onto the surfaces of large objects, and a light stripe model of saturated laser stripes is proposed to extract the feature points of the stripes. Then, a ?exible laser-aided pattern is designed, and a high-precision feature extraction method based on a region-of-interest (ROI) is developed, by which the local dimensions of large objects acquired from different views can be transformed into the same coordinate system to complete the measurement of the object’s overall dimensions. Experiments are performed both in a laboratory and a forging workshop; the laboratory experiments validate that measurements can be completed with only a small overlapping region (25%) in a ?eld of 8.6 m × 5.0 m with a precision of up to 0.10% using the method proposed in this paper. The experiments in the forging workshop show that the method is effective at measuring the height of a large hot forging.
? 2015 Elsevier B.V. All rights reserved.
1. Introd
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