Visual mapping using learned structural priors

We investigate a new approach to vision based mapping, in which single image structure recognition is used to derive strong priors for initialisation of higher-level primitives in the map. This can reduce state size and speed up the building of more meaningful maps. We focus on plane mapping and use a recognition algorithm to detect and estimate the 3D orientation of planar structures in key frames, which are then used as priors for initialising planes in the map. The recognition algorithm learns the relationship between such structure and appearance from training examples offline. We demonstrate the approach in the context of an EKF based visual odometry system. Preliminary results of experiments in urban environments show that the system is able to build large maps with significant planar structure at average frames rates of around 60 fps whilst maintaining good trajectory estimation. The results suggest that the approach has considerable potential.