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The transformation of coordinates allows for the conversion of coordinates from one geodetic system to another. Usually, the determination of transformation parameters is performed by the means of a least squares method. Unfortunately, the least squares method is not immune to outliers. It means that if, for any reason, some reference points are disturbed with gross errors or they belong to two different archival coordinate systems, transformation parameters will be estimated with those errors. Therefore, it is very important to identify incorrect data and remove them from the estimation process or decrease their influence on the estimated parameters. This problem can be solved by applying the M split estimation to calculate transformation parameters. The method of estimation adopted in the paper allows the determination of two competitive vectors of transformation parameters and two competitive residual vectors. The suitability of using the M split estimation method in the process of coordinate transformation was tested on a real geodetic network. In the 'M split estimation' section the authors presents the idea of M split estimation, along with its application to estimation of transformation parameters. The authors performed the calculations in three scenarios: with different number, value and distribution of gross errors respectively. The results of the transformations compared with the catalogue value of coordinates, as well as the differences between coordinates after Helmert transformation, the M split transformation (for the vector of parameters V a ), the M split transformation (for the vector of parameters V b ) and the catalogue coordinates are presented in the 'Numerical example' section.

Historic buildings, due to their architectural, cultural, and historical value, are the subject of preservation and conservatory works. Such operations are preceded by an inventory of the object. One of the tools that can be applied for such purposes is Light Detection and Ranging (LiDAR). This technology provides information about the position, reflection, and intensity values of individual points; thus, it allows for the creation of a realistic visualization of the entire scanned object. Due to the fact that LiDAR allows one to ‘see’ and extract information about the structure of an object without the need for external lighting or daylight, it can be a reliable and very convenient tool for data analysis for improving safety and avoiding disasters. The main goal of this paper is to present an approach of automatic wall defect detection in unlit sites by means of a modified Optimum Dataset (OptD) method. In this study, the results of Terrestrial Laser Scanning (TLS) measurements conducted in two historic buildings in rooms without daylight are presented. One location was in the basement of the ruins of a medieval tower located in Dobre Miasto, Poland, and the second was in the basement of a century-old building located at the University of Warmia and Mazury in Olsztyn, Poland. The measurements were performed by means of a Leica C-10 scanner. The acquired dataset of x, y, z, and intensity was processed by the OptD method. The OptD operates in such a way that within the area of interest where surfaces are imperfect (e.g., due to cracks and cavities), more points are preserved, while at homogeneous surfaces (areas of low interest), more points are removed (redundant information). The OptD algorithm was additionally modified by introducing options to detect and segment defects on a scale from 0 to 3 (0—harmless, 1—to the inventory, 2—requiring repair, 3—dangerous). The survey results obtained proved the high effectiveness of the modified OptD method in the detection and segmentation of the wall defects. The values of area of changes were calculated. The obtained information about the size of the change can be used to estimate the costs of repair, renovation, and reconstruction.

Detection of bio-deterioration and moisture is one of the most important tasks for comprehensive diagnostic measurements of buildings and structures. Any undesirable change in the material properties caused by the action of biological agents contributes to gradual aesthetic and physical damage to buildings. Very often, such surface changes can lead to structural defects or poor maintenance. In this paper, radiometric analysis of point clouds is proposed for moisture and biofilm detection in building walls. Recent studies show that remote terrestrial laser scanning (TLS) technology is very useful for registering and evaluating the technical state of the deterioration of building walls caused by moisture and microorganisms. Two different types of TLS, time-of-flight and phase-shift scanners, were used in the study. The potential of TLS radiometric data for detecting moisture and biofilm on wall surfaces was tested on two buildings. The main aim of the research is to compare two types of scanners in the context of their use in the detection of moisture and microorganisms.

Over the years there have been a number of different computational methods that allow for the identification of outliers. Methods for robust estimation are known in the set of M-estimates methods (derived from the method of Maximum Likelihood Estimation) or in the set of R-estimation methods (robust estimation based on the application of some rank test). There are also algorithms that are not classified in any of these groups but these methods are also resistant to gross errors, for example, in M-split estimation. Another proposal, which can be used to detect outliers in the process of transformation of coordinates, where the coordinates of some points may be affected by gross errors, can be a method called RANSAC algorithm (Random Sample and Consensus). The authors present a study that was performed in the process of 2D transformation parameter estimation using RANSAC algorithm to detect points that have coordinates with outliers. The calculations were performed in three scenarios on the real geodetic network. Selected coordinates were burdened with simulated values of errors to confirm the efficiency of the proposed method. Keywords: Coordinate Transformation; RANSAC; Parameter Estimation. RESUMO

Building constructions are exposed to various forces and natural phenomena. Some of them are sudden and violent, e.g., an earthquake or heavy rains, causing a displacement of the ground. Other phenomena affect objects on a longer-term, e.g., vibrations caused by daily road traffic. Sometimes, building structures may have defects due to incorrect construction. In any case, if an engineering object shows changes in the relation to its correct geometry or position, deformation and displacement measurements are required. Engineering objects are also monitored during their construction. Nowadays, it is important to perform measurements quickly and with high accuracy. The use of a Terrestrial Laser Scanning (TLS) allows for the required measurement speed and accuracy. This measurement technology allows a large dataset, which can be arbitrarily elaborated, to be obtained. The structure of building objects can include vertices, lines, planes, and other shapes and can be described using mathematical functions. This allows data processing to be automated. In this article, we present the Msplit method as an effective approach to the processing of data obtained as a result of TLS measurements. The proposed approach is new because until now, the Msplit estimation method has not been used to detect adjacent planes in one-point cloud obtained from TLS. The Msplit estimation method allows a functional model to be split into two or more competitive models and thus two or more entities in a point cloud to be estimated simultaneously. Four different objects measured using TLS are presented: two objects representing vertical displacements and two objects representing horizontal displacements. The test results and analysis confirm that the Msplit estimation method can be successfully applied in the detection of adjacent planes.

Positioning systems are usually based on the satellite observations. However the traditional satellite positioning has some outage, it cannot be used inside buildings or underground. But there are some new technologies that allow for indoor positioning. Among those methods, there is a conception of the RF ranging technology. There are two basic methods of obtaining the distance in RF networks -time of flight (and its variations) and RSSI-based algorithms. The new method to obtain a distance between nodes in RF network is a new ranging approach based on the phase shift measurement. Analyzing the results of the measurements performed with this new technology, it was observed that a large number of them significantly differ from the reference value. Thus, in order to obtain correct distance filtering of the measurement results must be introduced to "smooth" the results. There is a necessity to find an algorithm that filters the observations. The authors present a proposition to use the RANSAC algorithm in the filtration process. In this paper the strategy of the use of RANSAC algorithm was presented. Analyzing the results of the measurement it was observed that a large number of measure distances are disturbed and significantly different from the reference value (test value). Thus to obtain the correct distance, filtering of the measurement results must be introduced in order to "smooth" the results. The authors present a RANSAC algorithm that allows to find the correct solution in noisy data. ARTICLE INFO EXPERIMENT SETUP DESCRIPTION

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