This paper presents a new feature point pairs based technique for object pose estimation and structure recovery from a single view. It first estimates rotational matrix independently, then computes translation vector and recovers the 3D structure of the object directly. Linear and nonlinear strategies are presented to estimate the rotational matrix. One is for small rotational motion and the other is used to estimate large rotational parameters. When the nonlinear technique is applied, its initial guesses are given automatically by the proposed linear estimation method. On the other hand, the presented structure recovery method is not sensitive to the rotational matrix estimation results. The proposed method is applicable to three, four or more feature points and has no constraints, such as collinear or coplanar, on their relative positions. As the number of feature points increases, the estimation results are improved while the computation cost is almost unchanged. Many experiments are performed on synthetic data and real images to demonstrate the presented technique.

This paper presents a localization method using motion vision. The proposed method locates a mobile robot relative to the object to which the robot moves. Two points are selected from the object as feature points. Consecutive two images containing the feature points are taken before and after the robot moves. Then, the poses of the robot can be determined according to the image coordinates of the feature points. A search algorithm is also presented to enhance the localization precision. It can find more correct image coordinates of the feature points based on the real detected image coordinates. Many experiments are performed on real images to justify this search algorithm. The results show that it is effective.