Towards Fully Dense Direct Filter-Based Monocular Visual-Inertial Odometry

Abstract

We propose a fully dense direct filter-based visual-inertial odometry method estimating both pixel depth for all pixels and robot state simultaneously, having all uncertainties in the same state vector. Due to the fully dense method, our approach works even in low-textured areas with very low, smooth gradients (i.e. scenes where feature based or semi-dense approaches fail). Our algorithm performs in real-time on a CPU with a time complexity linearly dependent on the amount of pixels in the provided image. To achieve this, we propose complexity reduction methods for fast matrix inversion, exploiting specific structures of the covariance matrix. We provide both simulated and real-world results in low-textured areas with a smooth gradient.