Assistance for Measuring Human-Made Structures with Robotic Total Stations

The history of total stations dates back to the 1970s, when manufacturers combined a theodolite with a laser distance meter for measuring angles and distances with high accuracy. The integration of multiple sensors over the years turned the system into a smart and powerful measurement device. Modern total stations consist of a variety of different sensors and actuators, a tracking system for reflective prisms, an embedded processor and a remote control unit to assist users with standard measuring tasks. Such systems allow assisted targeting and tracking, and apply automatic measurement corrections when using surveying prisms.
However, with all the sensors in place, traditional methods do not use the full potential of totals stations, especially when using the reflectorless measurement mode. In particular, the quality of measuring natural targets highly depends on the user experience.
In this work, we address measurement assistance systems for reflectorless robotic total stations in the field of surveying and building construction. To target a wide range of devices, we focus on systems that do not have explicit sensor data synchronization and do not rely on photogrammetry. Our methods increase the productivity and allows non-experts to perform accurate and reliable measurements.
In particular, we present an assistance system for accurate targeting of human-made structures with a robotic total station in reflectorless mode. We reduce the uncertainty and increase the reliability of corner and edge measurements by applying linear approximations of the measured structure in real-time.
Furthermore, we present an assisted reflectorless registration of a robotic total station and a CAD model. Here, we reduce the required user interaction, while retaining accurate and reliable registration in real-time.
In this work, we use a generalized description of robotic total stations based on robotic theory. We present all required steps for converting the system model into an efficient design and simulation ecosystem. This allows exploration of the problem and solution space beyond the limitations of particular hardware configurations, and seamless exchange of the simulator and physical devices for prototyping and concept verification.
In particular, we discuss how geometric models of robotic total stations can be extracted automatically by using the Denavit-Hartenberg convention. We discuss modeling concepts for various sensor and environment combinations and analyze the simulation uncertainty of an exemplary setup. In addition, we qualify the simulator according to the JCGM 100:2008 Guide to the Expression of Uncertainty (GUM), which describes the evaluation and report of physical measurements and their measurement uncertainties. This allows for a standardized comparison of similar systems and interpretation of simulation results. We also present an a-priori qualification method, which allows specifying crucial parameters for simulation setups in advance to the actual implementation. The method is intended to serve researchers, software and hardware developers as a guide for designing simulation and verification systems with similar properties.