Evaluating Pose Estimation Uncertainty From Automatic Point Cloud Registration in 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, for example, 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 indoor and an outdoor experiment, involving the use of a total station and a laser tracker. The latter was mounted on a Husky A200 UGV together with a RIEGL 3D terrestrial laser scanner. Our method enables the determination of the relative poses with respect to a superordinate system with 0.1 mm and 0.02 mrad indoors, as well as 0.3 mm and 0.04 mrad outdoors on average. The poses of the automatic registration differ from the reference poses in both experiments up to a maximum of 3 mm and 0.8 mrad. Taking into account the initial misalignment between the two realizations of the common coordinate system, the pose differences with respect to the first pose are within the same range.