Visual-Inertial Fusion on KITTI using MSF-EKF

In this article, we propose a loose-coupling approach between the accurate Parallax Beam (PB) algorithm and an Inertial Measurement Unit (IMU) for a robust, off-road and system agnostic navigation application. With the aim of achieving better robustness against image-shortage and motion-estimation errors while keeping PB accuracy, we propose the use of the Multi-Sensor Fusion Extended Kalman Filter (MSF-EKF). The latter uses asynchronous data provided by both the IMU and PB with different sampling rates to perform a high rate estimation able to ensure correct navigation during image shortage. The evaluation performed on the KITTI vision benchmark suite shows promising results with a good accuracy and is able to run with a sampling rate ten times faster than the sampling rate of Parallax Beam.