The poses derived from automatic point cloud registration between stationary laser scans have the potential to be incorporated in the trajectory estimation of robotic multi-sensor systems operating in stop-and-go mode. Such relative poses are valuable for applications like automated capturing of the natural and built environment, i.e. mobile mapping. Generating accurate ground truth data for the registered poses is an essential part of their uncertainty evaluation. Due to the high accuracy performance of point cloud registration methods, ground truth data with sub-mm accuracy in position is necessary. In a previous study (Brandstätter et al. 2024), an approach has been developed to obtain corresponding ground truth data with standard deviations of a few tens of mm. However, the method developed is very resource-intensive, as it involves measurements from a laser tracker, a terrestrial laser scanner and a total station. The first two instruments are mounted on a robotic platform. The calculation of the ground truth poses is based on a weighted least squares adjustment. Expensive sensors and the elaborate measurement process impede the straightforward implementation of our approach in practice. Therefore, this paper assesses different measures to increase resource efficiency. The goal is to obtain sufficiently accurate ground truth data despite simplifying the initial, extensive measurement concept, e.g. through excluding the total station or the laser tracker. Excluding the total station measurements from the extensive approach increases the estimated standard deviations for the laser scanner poses establishing the ground truth up to 0.3 mm in translation and 3 mgon in rotation. Additionally, replacing the laser tracker with a total station results in a maximum increase in standard deviations of 1 mm and 15 mgon, respectively, for the ground truth poses compared to the extensive approach.