Abstract
As an important part of industrial 3D scanning, a relocation algorithm is used to restore the position and the pose of a 3D scanner or to perform closed-loop detection. The real time and the relocation correct ratio are prominent and difficult points in 3D scanning relocation research. By utilizing the depth map information captured by a binocular vision 3D scanner, we developed an efficient and real-time relocation algorithm to estimate the current position and pose of the sensor real-time and high-correct-rate relocation algorithm for small-range 3D texture less scanning. This algorithm mainly involves feature calculation, feature database construction and query, feature matching verification, and rigid transformation calculation; through the four parts, the initial position and pose of the sensors in the global coordinate system is obtained. In the experiments, the efficiency and the correct-rate of the proposed relocation algorithm were elaborately verified by offline and online experiments on four objects of different sizes, and a smooth and a rough surface. With more data frames and feature points, the relocation could be maintained real time within 200 ms, and a high correct rate of more than 90% could be realized. The experimental results showed that the proposed algorithm could achieve a real-time and high-correct-ratio relocation.
Highlights
Some high-precision three-dimensional (3D) handheld scanners, such as GoScan [1], EinScan-Pro+ [2], and GSCAN [3] have been used in 3D modeling, industrial inspection, and reverse design
We evaluated the proposed relocation pipeline on the data collected by a small-range visual scanner
The four objects were selected from the types of objects that the scanner often scans in actual use
Summary
Some high-precision three-dimensional (3D) handheld scanners, such as GoScan [1], EinScan-Pro+ [2], and GSCAN [3] have been used in 3D modeling, industrial inspection, and reverse design. These 3D scanners are based on the binocular stereo visual technology, which captures the two images with structured light specular once and generates the depth map by using a stereo matching method. These devices can generate a more accurate 3D depth map because of the smaller scanning distance. The information that we can use is only the images captured by the cameras and the depth map information obtained through stereo matching
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