Monocular visual odometry / SLAM requires the ability to deal with the scale ambiguity problem, or equivalently to transform the estimated unscaled poses into correctly scaled poses. While propagating the scale from frame to frame is possible, it is very prone to the scale drift effect. We address the problem of monocular scale estimation by proposing a multimodal mechanism of prediction, classification, and correction. Our scale correction scheme combines cues from both dense and sparse ground plane estimation; this makes the proposed method robust towards varying availability and distribution of trackable ground structure. Instead of optimizing the parameters of the ground plane related homography, we parametrize and optimize the underlying motion parameters directly. Furthermore, we employ classifiers to detect scale outliers based on various features (e.g. moments on residuals). We test our method on the challenging KITTI dataset and show that the proposed method is capable to provide scale estimates that are on par with current state-of-the-art monocular methods without using bundle adjustment or RANSAC.