Monocular Depth Estimation enhancement by depth from SLAM Keypoints

Training a neural network in a supervised way is extremely challenging since ground truth is expensive, time consuming and limited. Therefore the best choice is to do it unsupervisedly, exploiting easier-to-obtain binocular stereo images and epipolar geometry constraints. Sometimes however, this is not enough to predict fairly correct depth maps because of ambiguity of colour images, due for instance to shadows, reflective surfaces and so on. A Simultaneous Location and Mapping (SLAM) algorithm keeps track of hundreds of 3D landmarks in each frame of a sequence. Therefore, given the base assumption that it has the right scale, it can help the depth prediction providing a value for each of those 3D points. This work proposes a novel approach to enhance the depth prediction exploiting the potential of the SLAM depth points to their limits.