A wide range of robotic systems needs to estimate their rotation for diverse tasks like automatic control and stabilization, among many others. In regards of the limitations of traditional navigation equipments (like GPS and inertial sensors), this paper follows a vision approach based on the observation of vanishing points (VPs). Urban environments (outdoor as well as indoor) generally contain orthogonal VPs which constitutes an important constraint to fulfill in order to correctly acquire the

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... structure of the scenes. In contrast to existing VP-based techniques, our method inherently enforces the orthogonality of the VPs by directly incorporating the orthogonality constraint into the model estimation step of the RANSAC procedure, which allows real-time applications. The model is estimated from only 3 lines, which corresponds to the theoretical minimal sampling for rotation estimation and constitutes our 3-line RANSAC. We also propose a 1-line RANSAC when the horizon plane is known. Our algorithm has been validated successfully on challenging real datasets.