  In this paper we deal with the initialization problem of a visual-inertial
odometry system with rolling shutter cameras. Initialization is a prerequisite
for using inertial signals and fusing them with visual data. We propose a novel
statistical solution to the initialization problem on visual and inertial data
simultaneously, 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 the necessary
steps and methodology specific to the initialization problem. Extensive
evaluations on ground truth exhibit superior performance and a gain in accuracy
of up to $20\%$ over the originally proposed Least Squares solution. The
renormalization performs similarly to the optimal Maximum Likelihood estimate,
despite arriving at the solution by different means. With this paper we are
adding to the set of Computer Vision problems which can be cast into the
renormalization scheme.
