Active 6-D position-pose estimation of a spatial circle using monocular eye-in-hand system

Nuts and bolts are common components in assembly lines. Their position and pose estimation is a vital step for automatic assembling. Although many approaches using a monocular camera have been proposed, few works consider a monocular camera’s active movements for improving estimation accuracy. This article presents an active movement strategy for a monocular eye-in-hand camera for high position and pose estimation accuracy of a spatial circle. Extensive experiments are conducted to validate the effectiveness of the proposed method for position and pose estimation of circles printed on paper, real circular flat washers, and nuts.

Stable Position and Pose Estimation of Industrial Parts Using Evaluation of Observability of 3D Vector Pairs

<div class=""abs_img""> <img src=""[disp_template_path]/JRM/abst-image/00270002/07.jpg"" width=""300"" /> Recognition results</div> This paper introduces a stable 3D object detection method that can be applied to complicated scenes consisting of randomly stacked industrial parts. The proposed method uses a 3D vector pair that consists of paired 3D vectors with a shared starting point. By considering the observability of vector pairs, the proposed method has achieved high recognition performance. The observability factor of the vector pair is calculated by simulating the visible state of the vector pair from various viewpoints. By integrating the observability factor and the distinctiveness factor proposed in our previous work, a few vector pairs that are effective for recognition are automatically extracted from an object model, and then used for the matching process. Experiments have confirmed that the proposed method improves the 88.5% recognition success rate of previous state-of-the-art methods to 93.1%. The processing time of the proposed method is fast enough for robotic bin-picking. </span>

Vision Autonomous Relative Positioning and Orientating Algorithm for Distributed Micro/Nanosatellite Earth Observation System Based on Dual Quaternion

It is a valid way to analyze the space object real-time movement by using distributed satellite earth observation system, which can provide the stereographic image through the collaboration of the satellites. That relative position and pose estimation is one of the key technologies for distributed micro/nanosatellite earth observation system (DMSEOS). In this paper, on the basis of the attitude dynamics of spacecrafts and the theory of machine vision, an autonomous positioning and orientating algorithm for distributed micro/nanosatellites based on dual quaternion and EKF (extended Kalman filtering) is proposed. Firstly, how to represent a line transform unit using dual quaternion is introduced. Then, the feature line point of the line transform unit is defined. And then, on the basis of the attitude dynamics of spacecrafts and the theory of EKF, we build the state and observation equations. Finally, the simulations show that this algorithm is an accurate valid method in positioning and orientating of distributed micro/nanosatellite earth observation system.