The warm-up effect is a well-known phenomenon, which occurs in all types of laser trackers. The series of experiments was performed to determine the influence of warm-up effect on measurement and a warm-up time of device – the time after the temperature inside the tracker is stable. In this paper, the tested tracker was Leica AT960-MR. Results showed that the warm-up effect could cause errors up to tenths of millimeters, and a warm-up time of instrument is around two hours, which is similar to the other researches.
The laser tracker is a widely used instrument in many industrial and metrological applications with high demand measurement accuracy. Imperfections in construction and misalignment of individual parts deliver systematic errors in the measurement results. All error sources need to be identified and reduced to the minimum to achieve the best possible accuracy. The paper summarizes error sources of the laser tracker without beam steering mirror with emphasis on error modeling. Descriptions of error models are provided for the static and kinematic type of measurement.
The results of terrestrial laser scanning are point clouds, which are becoming an increasingly common initial digital representation of real-world objects. Since point clouds in the most cases represent a huge amount of data, automation of the processing steps is advisable. The paper brings a short review of the most reliable methods of cylinder extraction. An innovative algorithm is proposed for an automated detection of cylinders and also for estimating their parameters from 3D point cloud data. The method was tested on the complex point clouds of pipelines. The proposed algorithm was implemented to a standalone application based on MATLAB® software.
Recently, attempts have been made to automate data acquisition, which is also related to efforts to automate data processing. The paper deals with the automation of terrestrial laser scanning data processing. The approaches for point cloud segmentation are briefly described. An algorithm based on random sample consensus is proposed for automated plane identification and plane segmentation from point clouds. The proposed approach was tested by processing point clouds; the results of the testing are also described. Based on the proposed algorithm, a standalone application for automated plane segmentation from laser scanner data using Matlab® software was developed.
Nowadays huge datasets can be collected in a relatively short time. After capturing these data sets the next step is their processing. Automation of the processing steps can contribute to efficiency increase, to reduction of the time needed for processing, and to reduction of interactions of the user. The paper brings a short review of the most reliable methods for sphere segmentation. An innovative algorithm for automated detection of spheres and for estimating their parameters from 3D point clouds is introduced. The algorithm proposed was tested on complex point clouds. In the last part of the paper, the implementation of the algorithm proposed to a standalone application is described.