Developing rigid constraint for the estimation of pose and structure from a single image

Pose and structure estimation from a single image is a fundamental problem in machine vision and multiple sensor fusion and integration. In this paper we propose using rigid constraints described in different coordinate frames to iteratively estimate structural and camera pose parameters. Using geometric properties of reflected correspondences we put forward a new concept, the reflected pole of a rigid transformation. The reflected pole represents a general analysis of transformations that can be applied to both 2D and 3D transformations. We demonstrate how the concept is applied to calibration by proposing an iterative method to estimate the structural parameters of objects. The method is based on a coarse-to-fine strategy in which initial estimation is obtained through a classical linear algorithm which is then refined by iteration. For a comparative study of performance, we also implemented an extended motion estimation algorithm (from 2D-2D to 3D-2D case) based on epipolar geometry.

Bionic Attitude Transformation Combined with Closed Motion for a Free Floating Space Robot

In order to realize the small error attitude transformation of a free floating space robot,a new method of three degrees of freedom（ DOF） attitude transformation was proposed for the space robot using a bionic joint. A general kinematic model of the space robot was established based on the law of linear and angular momentum conservation. A combinational joint model was established combined with bionic joint and closed motion. The attitude transformation of planar,two DOF and three DOF is analyzed and simulated by the model,and it is verified that the feasibility of attitude transformation in three DOF space. Finally,the specific scheme of disturbance elimination in attitude transformation is presented and simulation results are obtained.Therefore,the range of application field of the bionic joint model has been expanded.

Hand-eye calibration with a new linear decomposition algorithm

To solve the homogeneous transformation equation of the form AX=XB in hand-eye calibration, where X represents an unknown transformation from the camera to the robot hand, and A and B denote the known movement transformations associated with the robot hand and the camera, respectively, this paper introduces a new linear decomposition algorithm which consists of singular value decomposition followed by the estimation of the optimal rotation matrix and the least squares equation to solve the rotation matrix of X. Without the requirements of traditional methods that A and B be rigid transformations with the same rotation angle, it enables the extension to non-rigid transformations for A and B. The details of our method are given, together with a short discussion of experimental results, showing that more precision and robustness can be achieved.

A geometric reasoning based algorithm for point pattern matching

Point pattern matching (PPM) is an important topic in computer vision and pattern recog-nition . It can be widely used in many areas such as image registration, object recognition, motion de-tection, target tracking, autonomous navigation, and pose estimation. This paper discusses the in-complete matching problem of two point sets under Euclidean transformation. According to geometric reasoning, some definitions for matching clique, support point pair, support index set, and support in-dex matrix, etc. are given. Based on the properties and theorems of them, a novel reasoning algo-rithm is presented, which searches for the optimal solution from top to bottom and could find out as many consistent corresponding point pairs as possible. Theoretical analysis and experimental results show that the new algorithm is very effective, and could be, under some conditions, applied to the PPM problem under other kind of transformations.