We present a novel method to recognise planar structures in a single image and estimate their 3D orientation. This is done by exploiting the relationship between image appearance and 3D structure, using machine learning methods with supervised training data. As such, the method does not require specific features or use geometric cues, such as vanishing points. We employ general feature representations based on spatiograms of gradients and colour, coupled with relevance vector machines for classification and regression. We first show that using hand-labelled training data, we are able to classify pre-segmented regions as being planar or not, and estimate their 3D orientation. We then incorporate the method into a segmentation algorithm to detect multiple planar structures from a previously unseen image.

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.

We propose an algorithm to detect planes in a single image of an outdoor urban scene, capable of identifying multiple distinct planes, and estimating their orientation. Using machine learning techniques, we learn the relationship between appearance and structure from a large set of labelled examples. Plane detection is achieved by classifying multiple overlapping image regions, in order to obtain an initial estimate of planarity for a set of points, which are segmented into planar and non-planar regions using a sequence of Markov random fields. This differs from previous methods in that it does not rely on line detection, and is able to predict an actual orientation for planes. We show that the method is able to reliably extract planes in a variety of scenes, and compares favourably with existing methods.