Uncertainty Assessment of Poses Derived from Automatic Point Cloud Registration in the Context of Stop-and-Go Multi Sensor Robotic Systems

Abstract

The poses derived from automatic point cloud registration between stationary laser scans that occur naturally in robotic multi-sensor systems operating in stop-and-go mode have the potential to greatly improve and aid localization and trajectory estimation. The uncertainty assessment of these registered poses is crucial for its correct system integration and utilization, e.g., for mobile mapping applications. However, obtaining ground truth data with sufficient accuracy for both the position and attitude remains challenging. In this paper, we present a novel measurement setup and a data fusion method based on an iterative weighted least squares adjustment to assess the quality of such derived relative poses. The approach is tested and demonstrated in an outdoor experiment covering a driving area of approximately 65 × 7 m. A total station and a laser tracker were used to realize the ground truth. The latter was mounted on a Husky A200 UGV together with a Riegl VZ-600i laser scanner. The realized ground truth enables the determination of the relative poses with respect to a superordinate system with 0.3 mm and 0.04 mrad on average. The relative poses of the automatic registration differ from the reference poses up to a maximum of 3 mm and 0.76 mrad.