In an autonomous navigation context, a precise localisation of the vehicule is important to ensure a reliable navigation. Low cost sensors such as GPS systems are inacurrate and inefficicent in urban areas, and therefore the employ of such sensors alone is not well suited for autonomous navigation. On the other hand, camera sensors provide a dense photometric measure that can be processed to obtain both localisation and mapping information. In the robotics community, this problem is well known as Simultaneous Localisation and Mapping (SLAM) and it has been studied for the last thirty years. In general, SLAM algorithms are incremental and prone to drift, thus such methods may not be efficient in large scale environments for real-time localisation. Clearly, an a-priori 3D model simplifies the localisation and navigation tasks since it allows to decouple the structure and motion estimation problems. Indeed, the map can be previously computed during a learning phase, whilst the localisation can be handled in real-time using a single camera and the pre-computed model. Classic global 3D model representations are usually inacurrate and photometrically inconsistent. Alternatively, it is proposed to use an ego-centric model that represents, as close as possible, real sensor measurements. This representation is composed of a graph of locally accurate spherical panoramas augmented with dense depth information. These augmented panoramas allow to generate varying viewpoints through novel view synthesis. To localise a camera navigating locally inside the graph, we use the panoramas together with a direct registration technique. The proposed localisation method is accurate, robust to outliers and can handle large illumination changes. Finally, autonomous navigation in urban environments is performed using the learnt model, with only a single camera to compute localisation.
