Traversability analysis using terrain mapping and online-trained Terrain type classifier

Path estimation is a big challenge for autonomous vehicle navigation, especially in unknown, dynamic environments, when road characteristics change often. 3D terrain information (e.g. stereo cameras) can provide useful hints about the traversability cost of certain regions. However, when the terrain tends to be flat and uniform, it is difficult to identify a better path using 3D map solely. In this scenario the use of a priori knowledge on the expected road's visual characteristics can support detection, but it has the drawback of being not robust to environmental changes. This paper presents a path detection method that mixes together 3D mapping and visual classification, trying to learn, in real time, the actual road characteristics. An on-line learning of visual characteristics is implemented to feedback a terrain classifier, so that the road characteristics are updated as the vehicle moves. The feedback data are taken from a 3D traversability cost map, which provides some hints on traversable and non-traversable regions. After several re-training cycles the algorithm converges on a better separation of the path and non-path regions. The fusion of both 3D traversability cost and visual characteristics of the terrain yields a better estimation when compared with either of these methods solely.