  Navigation in unknown, chaotic environments continues to present a
significant challenge for the robotics community. Lighting changes,
self-similar textures, motion blur, and moving objects are all considerable
stumbling blocks for state-of-the-art vision-based navigation algorithms. In
this paper we present a novel technique for improving localization accuracy
within a visual-inertial navigation system (VINS). We make use of training data
to learn a model for the quality of visual features with respect to
localization error in a given environment. This model maps each visual
observation from a predefined prediction space of visual-inertial predictors
onto a scalar weight, which is then used to scale the observation covariance
matrix. In this way, our model can adjust the influence of each observation
according to its quality. We discuss our choice of predictors and report
substantial reductions in localization error on 4 km of data from the KITTI
dataset, as well as on experimental datasets consisting of 700 m of indoor and
outdoor driving on a small ground rover equipped with a Skybotix VI-Sensor.
