Edge-based visual-inertial odometry

Abstract

In this paper we propose a method for monocular visual-inertial odometry that utilizes image edges as measurements. In contrast to previous feature-based approaches, the proposed method does not employ any assumption on the geometry of the scene (e.g., it does not assume straight lines). It can thus use measurements from all image areas with significant gradient, similarly to direct semi-dense methods. However, in contrast to direct semi-dense approaches, the proposed method's measurement model is invariant to linear changes in the image intensity. The novel edge parameterization and measurement model we propose explicitly account for the fact that edge points can only provide useful information in the direction of the image gradient. We present both Monte-Carlo simulations, as well as results from real-world experimental testing, which demonstrate that the proposed edge-based approach to visual-inertial odometry is consistent, and outperforms the point-based one.