Accurate outdoor ground truth based on total stations

Abstract

In the context of robotics, accurate ground truth positioning is essential for the development of Simultaneous Localization and Mapping (SLAM) and control algorithms. Robotic Total Stations (RTSs) provide accurate and precise reference positions in different types of outdoor environments, especially when compared to the limited accuracy of Global Navigation Satellite System (GNSS) in cluttered areas. Three RTSs give the possibility to obtain the six-Degrees Of Freedom (DOF) reference pose of a robotic platform. However, the uncertainty of every pose is rarely computed for trajectory evaluation. As evaluation algorithms are getting increasingly precise, it becomes crucial to take into account this uncertainty. We propose a method to compute this six-DOF uncertainty from the fusion of three RTSs based on Monte Carlo (MC) methods. This solution relies on point-to-point minimization to propagate the noise of RTSs on the pose of the robotic platform. Five main noise sources are identified to model this uncertainty: noise inherent to the instrument, tilt noise, atmospheric factors, time synchronization noise, and extrinsic calibration noise. Based on extensive experimental work, we compare the impact of each noise source on the prism uncertainty and the final estimated pose. Tested on more than 50 km of trajectories, our comparison highlighted the importance of the calibration noise and the measurement distance, which should be ideally under 75 m. Moreover, it has been noted that the uncertainty on the pose of the robot is not prominently affected by one particular noise source, compared to the others.

Kilometer-scale autonomous navigation in subarctic forests: challenges and lessons learned

“…The data were post-processed offline using RTKLIB 4 (Takasu & Yasuda, 2009) to produce the most accurate measurements possible. To estimate the GNSS localization error, we compared the manually measured distance between the GNSS receivers mounted on the UGV with the distance indicated by the GNSS localization, similarly as in (Vaidis et al, 2021). This metric does not take into account localization biases to which both receivers might be subjected, thus we consider the error metric an optimistic one.…”

System and Method for Reducing NLOS Errors in UWB Indoor Positioning

“…In outdoor environments, RTSs are the preferred choice to obtain high-accuracy measurements in the order of millimeters. In indoor robot navigation, their contribution is marked by high precision and stability [28]. However, few studies use RTS to correct UWB locations.…”

Generation of Reference Vehicle Trajectories in Real-World Situations Using Aerial Imagery From a Helicopter

“…Therefore, new methods for the determination of the reference trajectory need to be developed facilitating the characterisation of GNSS-INS-based reference solutions. There exist relative positioning methods with robotic total stations (Vaidis et al, 2021) which require three synchronised robotic stations at measured positions that can track prisms mounted on the target vehicle. Based on three reference positions with synchronised relative measurements of distance, azimuth and elevation to the target prisms the reference trajectory can be calculated.…”