  Recent achievements in depth prediction from a single RGB image have powered
the new research area of combining convolutional neural networks (CNNs) with
classical simultaneous localization and mapping (SLAM) algorithms. The depth
prediction from a CNN provides a reasonable initial point in the optimization
process in the traditional SLAM algorithms, while the SLAM algorithms further
improve the CNN prediction online. However, most of the current CNN-SLAM
approaches have only taken advantage of the depth prediction but not yet other
products from a CNN. In this work, we explore the use of the outlier mask, a
by-product from unsupervised learning of depth from video, as a prior in a
classical probability model for depth estimate fusion to step up the
outlier-resistant tracking performance of a SLAM front-end. On the other hand,
some of the previous CNN-SLAM work builds on feature-based sparse SLAM methods,
wasting the per-pixel dense prediction from a CNN. In contrast to these sparse
methods, we devise a dense CNN-assisted SLAM front-end that is implementable
with TensorFlow and evaluate it on both indoor and outdoor datasets.
