Despite the increasing interest in kinematic data acquisition, Robotic Total Stations (RTSs) are still relatively seldom used. No matter if Mobile Mapping Systems or Control & Guidance, GNSS is mostly used as position sensor, which limits the application to outdoor areas. For indoor applications, a combination of relative sensors is usually employed. One reason why RTSs are not used is the challenging time referencing and synchronization. In this work we analyze the challenges of a synchronized kinematic application of RTSs and present solutions. Our approach is based on a wireless network synchronization to establish a precise temporal reference frame. The achievable synchronization quality is thoroughly examined. In addition we develop a kinematic model of spherical measurements, that incorporates timing related parameters. To estimate these parameters we propose a temporal calibration utilizing an industrial robot. Both parts of our approach are evaluated using a test setup of two total stations, proofing an overall synchronization accuracy of 0.2 ms. An overall horizontal kinematic point accuracy of 2.3 mm reveals the potential of sufficiently synchronized RTSs.

This paper presents a novel approach for sensor fusion of robotic total station (RTS) and inertial navigation system (INS) to enable 6-degree-of-freedom (6DoF) pose estimation. Tight coupling of a spherical measurement model for RTS is developed, providing advantages over the traditional cartesian 3D-position measurement model, including supporting INS solution when distance measurements are unavailable and performing outlier detection in spherical observation space. Simulation studies demonstrate that replacing Global Navigation Satellite Systems (GNSS) with RTS for fusion with INS is beneficial in any environment (given line-of-sight (LOS) availability), even under ideal GNSS conditions. Furthermore, investigations on measurement models and failure identification over the entire range of RTS measurements reveal that the spherical model is advantageous over the cartesian model in certain regions. The developed methods are validated in a practical application for tilt compensation of an RTS pole, indicating a base 2D-RMSE of 3.8 mm for almost static and almost vertical poles, increasing with tilt and velocity.

In our contribution, we conduct a thematic literature review on trajectory estimation using a decentralized multi-sensor system based on robotic total stations (RTS) with a focus on unmanned aerial system (UAS) platforms. While RTS are commonly used for trajectory estimation in areas where GNSS is not sufficiently accurate or is unavailable, they are rarely used for UAS trajectory estimation. Extending the RTS with integrated camera images allows for UAS pose estimation (position and orientation). We review existing research on the entire RTS measurement processes, including time synchronization, atmospheric refraction, prism interaction, and RTS-based image evaluation. Additionally, we focus on integrated trajectory estimation using UAS onboard measurements such as IMU and laser scanning data. Although many existing articles address individual steps of the decentralized multi-sensor system, we demonstrate that a combination of existing works related to UAS trajectory estimation and RTS calibration is needed to allow for trajectory estimation at sub-$\mathrm{c}$$\mathrm{m}$ and sub-$0.01$ gon accuracies, and we identify the challenges that must be addressed. Investigations into the use of RTS for kinematic tasks must be extended to realistic distances (approx. 300–500 m) and speeds (>2.5 m s⁻¹). In particular, image acquisition with the integrated camera must be extended by a time synchronization approach. As to the estimation of UAS orientation based on RTS camera images, the results of initial simulation studies must be validated by field tests, and existing approaches for integrated trajectory estimation must be adapted to optimally integrate RTS data.

In Engineering Geodesy, most coordinate frames are aligned with the local vertical. For many measurement tasks, it is therefore necessary to manually (or arithmetically) align sensors or equipment with the local vertical, which is a common source of errors and it is very time consuming. Alternatively, accelerometer triads as part of inertial measurement units (IMUs) are used in several applications for horizon leveling. In this contribution we analyze and develop a method to use accelerometer triads for pole tilt compensation with total stations. Several triad sensor models are investigated and applied in a calibration routine using an industrial robot arm. Furthermore a calibration routine to determine the orientation of the IMU mounted on the pole is proposed. Using variance based sensitivity analysis we investigate the influence of different model parameters on leveling and pole tilt compensation. Based on this inference the developed calibration routines are adjusted. The final evaluation experiment shows an RMS of 2.4 mm for the tilt compensated measured ground point with tilts up to 50 gon.

Accurate and reliable reference data are key elements for advancing remote sensing techniques and validating the quality of geo data. This includes remote sensing methods for bathymetric data collection, such as SONAR or bathymetric LiDAR. In this work, we present a method for acquiring accurate bathymetric reference data for shallow water depths. We use a tilt-compensating dual-prism measurement pole combined with two time-synchronized robotic total stations to allow tilted and non-static pole measurements. The accessible water depth of our method is restricted only by the pole length. The error induced by pole tilt and movement, caused by water currents, large water depths, or the operation from unstable vehicles, is minimized by using the time-synchronized tilt information gathered from two reflectors mounted on the measurement pole. With our approach, we show in an air-only experiment that we achieve better than 1 cm height RMSE and 8 cm position RMSE for tilted measurement with a 4.65 m long pole. The variance propagation and precision values derived from our in-water study suggest an even lower position RMSE of 3 cm.