Sky/ground modeling for autonomous MAV flight

Recently, we have implemented a computer-vision based horizon-tracking algorithm for flight stability and autonomy in micro air vehicles (MAVs) [S. M. Ettinger et al., 2002]. Occasionally, this algorithm fails in scenarios where the underlying Gaussian assumption for the sky and ground appearances is not appropriate. Therefore, in this paper, we present a general statistical image modeling framework which we have use to build prior models of the sky and ground. Once trained, these models can be incorporated into our existing horizon-tracking algorithm. Since the appearances of the sky and ground vary enormously, no single feature is sufficient for accurate modeling: as such, we rely both on color and texture as critical features in our modeling framework. Specifically, we choose hue and intensity for our color representation, and the complex wavelet transform (CWT) for our texture representation. We then use hidden Markov tree (HMT) models, which are particularly well suited for the CWT's inherent tree structure, as our underlying statistical models over our feature space. With this approach, we have achieved reliable and robust image segmentation of flight images from on-board our MAVs as well as on more difficult-to-classify sky/ground images.