An Equivariant Filter for Visual Inertial Odometry
This work addresses VIO for devices with cameras and IMUs, offering an incremental improvement over existing EKF-based methods.
The authors tackled the Visual Inertial Odometry (VIO) problem by proposing a novel geometric formulation on a quotient manifold and applying an Equivariant Filter (EqF), resulting in state-of-the-art performance on the EuRoC dataset compared to other EKF-based VIO algorithms.
Visual Inertial Odometry (VIO) is of great interest due the ubiquity of devices equipped with both a monocular camera and Inertial Measurement Unit (IMU). Methods based on the extended Kalman Filter remain popular in VIO due to their low memory requirements, CPU usage, and processing time when compared to optimisation-based methods. In this paper, we analyse the VIO problem from a geometric perspective and propose a novel formulation on a smooth quotient manifold where the equivalence relationship is the well-known invariance of VIO to choice of reference frame. We propose a novel Lie group that acts transitively on this manifold and is compatible with the visual measurements. This structure allows for the application of Equivariant Filter (EqF) design leading to a novel filter for the VIO problem. Combined with a very simple vision processing front-end, the proposed filter demonstrates state-of-the-art performance on the EuRoC dataset compared to other EKF-based VIO algorithms.