Delta filter - robust visual-inertial pose estimation in real-time: A multi-trajectory filter on a spherical mobile mapping system

Many state-of-the-art mobile mapping systems accomplish reliable and robust pose estimation utilizing combinations of inertial measurement units (IMUs), global navigation satellite systems (GNSS), visual-inertial- or LiDAR-inertial odometry (VIO/LIO). However, on a spherical mobile mapping system the underlying inherent rolling motion introduces high angular velocities, thus the quality of pose estimates, images, and laser-scans, degrade. In this work we propose a pose filter design that is able to do real-time sensor fusion between two unreliable trajectories into one, more reliable trajectory. It is a simple yet effective filter design that does not require the user to estimate the uncertainty of the sensors. The approach is not limited to spherical robots and theoretically is also suitable for sensor fusion of an arbitrary number of estimators. This work compares this filter against two pose estimation methods on our spherical system: (1) An approach that is based solely on IMU measurements, and (2) stereo-VIO with an Intel (R) RealSense (TM) tracking camera. The proposed "Delta" filter takes as input (1), (2), and a motion model. Our implementation gets rid of the drift in (1) and (2), estimates the scale of the trajectory, and deals with slow and fast motion as well as driving curves. Our source code can be found on github [1].