Delta- and Kalman-filter designs for multi-sensor pose estimation on spherical mobile mapping systems

Spherical mobile mapping systems are not thoroughly studied in terms of inertial pose estimation filtering. The underlying inherent rolling motion introduces high angular velocities and aggressive system dynamics around all principal axes. This motion profile also needs different modeling compared to state-of-the-art competitors, which heavily focus on more rotationally-restricted systems such as UAV, handheld, or cars. In this work we compare our previously proposed “Delta-filter”, which was heavily motivated by the sensors inability to provide covariance estimations, with a Kalman-filter design using a covariance model. Both filters fuse two 6-DoF pose estimators with a motion model in real-time, however the designs are theoretically suitable for an arbitrary number of estimators. We evaluate the trajectories against ground truth pose measurement from an OptiTrack™ motion capturing system. Furthermore, as our spherical systems are equipped with laser-scanners, we evaluate the resulting point clouds against ground truth maps available from a Riegl VZ400 terrestrial laser-scanner (TLS). Our source code and datasets can be found on github (Arzberger, 2023). • A Kalman-filter (KF) for multi-sensor pose estimation on spherical systems. • Covariance heuristics for spherical systems when sensors don’t provide covariances. • Point-clouds from fast rolling using our KF as an initial guess for ICP. • Evaluation of the KF using ground-truth pose and point-cloud data.