In many geodetic engineering applications it is necessary to solve the problem of describing a measured data point cloud, measured, e. g. by laser scanner, by means of free-form curves or surfaces, e. g., with B-Splines as basis functions. The state of the art approaches to determine B-Splines yields results which are seriously manipulated by the occurrence of data gaps and outliers.Optimal and robust B-Spline fitting depend, however, on optimal selection of the knot vector. Hence we combine in our approach Monte-Carlo methods and the location and curvature of the measured data in order to determine the knot vector of the B-Spline in such a way that no oscillating effects at the edges of data gaps occur. We introduce an optimized approach based on computed weights by means of resampling techniques. In order to minimize the effect of outliers, we apply robust M-estimators for the estimation of control points.The above mentioned approach will be applied to a multi-sensor system based on kinematic terrestrial laserscanning in the field of rail track inspection.

The process of surveying crane runways has been continually refined due to the competitive situation, modern surveying instruments, additional sensors, accessories and evaluation procedures. Guidelines, such as the International Organization for Standardization (ISO) 12488-1, define target values that must be determined by survey. For a crane runway these are for example the span, the position and height of the rails. The process has to be objective and reproducible. However, common processes of surveying crane runways do not meet these requirements sufficiently. The evaluation of the protocols, ideally by an expert, requires many years of experience. Additionally, the recording of crucial parameters, e.g., the wear of the rail, or the condition of the rail fastening and rail joints, is not regulated and for that reason are often not considered during the measurement. To solve this deficit the Advanced Rail Track Inspection System (ARTIS) was developed. ARTIS is used to measure the 3D position of crane rails, the cross-section of the crane rails, joints and, for the first time, the (crane-rail) fastenings. The system consists of a monitoring vehicle and an external tracking sensor. It makes kinematic observations with the tracking sensor from outside the rail run, e.g., the floor of an overhead crane runway, possible. In this paper we present stages of the development process of ARTIS, new target values, calibration of sensors and results of a test measurement.

B-spline curve approximation is a crucial task in many applications and disciplines. The most challenging part of B-spline curve approximation is the determination of a suitable knot vector. The finding of a solution for this multimodal and multivariate continuous nonlinear optimization problem, known as knot adjustment problem, gets even more complicated when data gaps occur. We present a new approach in this paper called an elitist genetic algorithm, which solves the knot adjustment problem in a faster and more precise manner than existing approaches. We demonstrate the performance of our elitist genetic algorithm by applying it to two challenging test functions and a real data set. We demonstrate that our algorithm is more efficient and robust against data gaps than existing approaches.

In engineering geodesy, the technical progress leads to various kinds of multi-sensor systems (MSS) capturing the environment. Multi-sensor systems, especially those mounted on unmanned aerial vehicles, subsequently called unmanned aerial system (UAS), have emerged in the past decade. Georeferencing for MSS and UAS is an indispensable task to obtain further products of the data captured. Georeferencing comprises at least the determination of three translations and three rotations. The availability and accuracy of Global Navigation Satellite System (GNSS) receivers, inertial measurement units, or other sensors for georeferencing is not or not constantly given in urban scenarios. Therefore, we utilize UAS-based laser scanner measurements on building facades. The building latter are modeled as planes in a three-dimensional city model. We determine the trajectory of the UAS by combining the laser scanner measurements with the plane parameters. The resulting implicit measurement equations and nonlinear equality constraints are covered within an iterated extended Kalman filter (IEKF). We developed a software simulation for testing the IEKF using different scenarios to evaluate the functionality, performance, strengths, and remaining challenges of the IEKF implemented. Georeferenzierung von Unmanned Aerial Systems mit Hilfe eines iterativen erweiterten Kalman Filters und eines 3D Gebäudemodells. In der Ingenieurgeodäsie führt der technische Fortschritt zu verschiedenen Arten von Multisensorsystemen (MSS), die der Erfassung der Umgebung dienen. In der vergangenen Dekade sind sehr viele MSS hinzugekommen, die auf einem Unmanned Aerial Vehicle montiert wurden. Diese MSS werden nachfolgend als Unmanned Aerial Systems (UAS) bezeichnet. Die Georeferenzierung von MSS und UAS ist ein notwendiger Schritt zur weiteren Datenverarbeitung. Die Georeferenzierung beinhaltet mindestens die Bestimmung von drei Translationen und drei Rotationen. Die erforderlichen Daten aus GNSS-Empfängern, inertialen Messsystemen oder anderen Sensoren zur Georeferenzierung sind in urbanem Umfeld nicht immer lückenlos und mit der erforderlichen Genauigkeit verfügbar. Deshalb werden in diesem Ansatz die Messungen UAS-basierter Laserscanner auf Gebäudefassaden verwendet. Letztere sind als Ebenen in einem 3D-Gebäudemodell modelliert. Die Trajektorie des UAS wird durch Kombination der Laserscanner-Messungen mit den Ebenenparametern ermittelt. Die daraus resultierenden impliziten Beobachtungsgleichungen und die nichtlinearen Restriktionsgleichungen werden innerhalb eines iterativen erweiterten Kalman-Filters (IEKF) modelliert. Außerdem wurde eine Softwaresimulation für den Test des IEKF entwickelt, um mit verschiedenen Szenarien die Funktionalität, Leistungsfähigkeit und verbleibende Herausforderungen zu bewerten.