Renormalization for Initialization of Rolling Shutter Visual-Inertial Odometry

In this paper we deal with the initialization problem of a visual-inertial odometry system with rolling shutter cameras. The initialization is a prerequisite to utilize inertial signals and fuse them with the visual data. We propose a novel way to solve this problem on visual and inertial data simultaneously in a statistical sense, by casting it into the renormalization scheme of Kanatani. The renormalization is an optimization scheme which intends to reduce the inherent statistical bias of common linear systems. We derive and present necessary steps and methodology specific for the initialization problem. Extensive evaluations on perfect ground truth exhibit superior performance and up to 20% accuracy gain to the originally proposed Least Squares solution. The renormalization performs similarly to the optimal Maximum Likelihood estimate, despite arriving to the solution by different means. By this, we extend the set of common Computer Vision problems which can be cast into the renormalization scheme.