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This paper describes a strategy to improve the accuracy of 3D position detection of a moving object. Stereo vision is one of powerful methods to detect a target position. However the measurement accuracy depends on the configuration of a target object and two cameras of vision system. In this case, these exist a camera configuration that enables to improve the measurement accuracy of object position. On the other hand, the camera system requires a multi-DOF motion to realize fast tracking of moving object. To address the above issues, a stereo vision system with multi-DOF motion is developed in this research. The constructed vision system has two hand-eye system that are controlled independently. In the proposed strategy, two performance indices are considered to determine the manipulator motion of hand-eye system. One is camera configuration index to evaluate the performance of the position detection by the stereo vision. The other is a manipulability measure of the manipulator in the hand-eye system. Considering both indices to determine the motion of hand-eye system, the tracking performance of stereo vision system is improved. Numerical and experimental results show the availability of the proposal method.

Abstract-This paper presents a novel approach to correct errors caused by accumulated scale drift in monocular SLAM. It is shown that the metric scale can be estimated using information gathered through monocular SLAM and image blur due to defocus. A nonlinear least squares optimization problem is formulated to integrate depth estimates from defocus to monocular SLAM. An algorithm to process the output keyframe and feature location estimates generated by a monocular SLAM algorithm to correct for scale drift at selected local regions of the environment is presented. The proposed algorithm is experimentally evaluated by processing the output of ORB-SLAM [1] to obtain accurate metric scale maps from a monocular camera without any prior knowledge about the scene.

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