The essence of pose estimation problem is how to determinate the relative position and orientation of two reference coordinate systems. The following are the main contents of the paper: The first chapter reviews the pose estimation problem and gives the purpose and contents of this study. The second chapter introduces some basic knowledge relative to pose estimation problem and proposes, taking the indoor mobile robot localization for example, a pose estimation method based on monocular vision and two feature points with the same height from the ground. The third chapter discusses how to estimate the pose of an object from monocular vision and three feature points. The proposed method can obtain close-form solution directly if the rotation of the object is small or computes rotational parameters iteratively by least-squares method with the initial values given by the close-form solution if the rotational parameters are large. The fourth chapter presents a pose estimation method based on monocular vision and four feature points aiming at the applications of exterior camera parameters calibration. The proposed method can calibrate a single camera or calibrate several cameras simultaneously. The fifth chapter proposes an indoor mobile robot localization method based on motion vision and two feature points with the same height from the floor. This technique can tackle the situation which cannot be solved from direct projection relation. The sixth chapter presents a novel depth estimation method, which can estimate the depths of feature points from arbitrary number of feature point pairs and has no restriction on the position relations of them. According to this algorithm, the pose estimation problems of the three situations of a camera motion, that is, the camera only translates, translates with small rotation and translates with large rotation, are discussed in detail.
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