Radon/Hough space for pose estimation

In this document, we present methods to camera pose estimation from one single images in a known environment. The framework of such methods comprises two stages, a learning step and an inference stage where given a new image we recover the exact camera position. This research work focus on achieving such a task with the help of lines and the Radon/Hough transform. The question to be answered in this study is what can be learnt from lines in order to compute a camera pose estimation. Firstly, we tried to point up a relationship between the Hough parameters of a set of lines (ρ, θ) and the camera pose in SE(3) -the space of rigid transformationsbased on KCCA method. Such a relationship could be used to predict pose estimation from line configurations. In a second approach, lines that are recovered in the radon space consist of our feature space. Such features are associated with [AdaBoost] learners that capture the wide image feature spectrum of a given 3D line. Such a framework is used through inference for pose estimation. Given a new image, we extract features which are consistent with the ones learnt, and we associate such features with a number of lines in the 3D plane that are pruned through the use of geometric constraints. Once correspondence between lines has been established, pose estimation is done in a straightforward fashion. Encouraging experimental results based on a real case are presented in this document.