A Stochastic Cloning Square-Root Information Filter with Accurate Feature Tracking for Visual-Inertial Odometry

Abstract

In this work, we introduce an enhanced square-root information filter for visual-inertial odometry. This filter utilizes stochastic cloning, implemented via Gaussian elimination, to facilitate time offset calibration and feature anchor changes. By using single-precision numbers within the filter, we significantly reduce computational load and memory requirements. In addition, we employ a fast Mahalanobis distance test and block Householder triangulation to accelerate the calculations. To mitigate feature drift from frame-to-frame optical flow, we create keyframes at regular intervals and refine long-tracked features between them. We use affine optical flow to compensate for patch deformations induced by possible large spatial transformations between keyframes. An analytical approach to computing the affine transformation is proposed. Experiments conducted on real-world data show that the proposed method achieves state-of-the-art performance at a much faster speed.