**3. Mixed-Degree Cubature** *H*∞ **Information Filter-Based VIO**

The above keyframe management method and measurement model of intensity led to a simple frame-to-frame alignment-based egomotion estimator. However, the simple estimator with linearization-based solvers such as the Gaussian–Newton optimization and EKF performed poorly as the models were highly nonlinear and the measurement noises vary dramatically. The linear approximations and Gaussian assumptions used in the solvers were not valid practically. For a robust estimator, the previous studies on VO generally maintained a slide window of multiple keyframes or a local map. In this section, we present a nonlinear filter-based VIO method for the frame-to-frame alignment problem. The designed hybrid cubature *H*∞ information filter used two cubature rules with different degrees to reduce the linearization error in a numerically stable way and the *H*∞ filter to estimate the states in the presence of non-Gaussian noises.
