Most 3D body scanners adopt a structure with multiple scanning sensors on the fixed frame to perform synchronous scanning from multiple directions of the human body. The position and attitude parameters of the multiple scanning sensors are the key to determining the 3D model stitching, and they are often calibrated when the scanner is installed. Any changes in these parameters of sensors will make model stitching errors and affect scanning accuracy. This paper studies a method for judging faults of 3D body scanner based on standard sphere. By scanning a standard sphere and observing the splicing deformation of the 3D model, it can determine whether the scanner's structure has changed, and identify which sensor has moved or rotated. Then the user can be guided to choose the appropriate calibration steps to compensate the scanner. Experiments prove that this method is a quick and effective intermediate check method of the 3D body scanner.
Accurate and traceable reference coordinates in three-dimensional space is the key and difficult point for coordinate calibration of large-scale measurement instruments such as laser tracker and iGPS. This paper studies the application of multilateration with laser tracker in establishing reference coordinates. First, a reference coordinate network is established, which has good spatial scalability and is compatible with multiple targets. Then, multilateration with laser tracker is applied to calibrate the reference coordinate network. And the basic principle, measurement uncertainty evaluation and tracker layout optimization are studied in detail. So that the reference coordinates are traced to the laser interference. Finally, through the repeatability test, length test, and coordinate test, it is shown that the reference coordinates satisfy their measurement uncertainty range and can be used for coordinate calibration of the large-scale measurement instruments.
The network geometry strongly influences the performance of the distributed system, i.e., the coverage capability, measurement accuracy and overall cost. Therefore the network placement optimization represents an urgent issue in the distributed measurement, even in large-scale metrology. This paper presents an effective computer-assisted network placement optimization procedure for the large-scale distributed system and illustrates it with the example of the multi-tracker system. To get an optimal placement, the coverage capability and the coordinate uncertainty of the network are quantified. Then a placement optimization objective function is developed in terms of coverage capabilities, measurement accuracy and overall cost. And a novel grid-based encoding approach for Genetic algorithm is proposed. So the network placement is optimized by a global rough search and a local detailed search. Its obvious advantage is that there is no need for a specific initial placement. At last, a specific application illustrates this placement optimization procedure can simulate the measurement results of a specific network and design the optimal placement efficiently.