Learning depth from appearance for fast one-shot 3-D map initialization in VSLAM systems

The aim of this work is to provide a fast approach for monocular SLAM initialization by constructing an initial 3-D map with interest points that are susceptible to be automatically tracked. Interest points' depth is inferred by means of a linear regression model, which estimates depth on the basis of local image appearance. Our contributions are: (1) a new scheme for learning and predicting associations between depth and local image appearance using RGB-D data; and (2) the use of this scheme for the initialization of state-of-the-art visual SLAM systems from a single image frame. To the best of our knowledge, this is the first attempt to automatically initialize a SLAM system by associating depth to sensor features through machine learning techniques. We performed a series of tests by making use of the celebrated PTAM system and obtained very promising results. We show successful one-shot initialization examples accomplished by applying our proposed approach to unstructured scene environments.