Dataset creation and selection methods with a wire drive flexible manipulator for vision-based reconstruction

Abstract

In order to improve spatial awareness for future investigations of reactor No. 2 at the Fukushima nuclear power plant, it is necessary first to acquire the environment model through reconstruction. To gather images for this task, we have developed a flexible, compact, and lightweight manipulator called the Bundled Wire Drive robot. However, due to mechanism’ shortcomings, the feasibility of using this robot is limited by potential degradations of image quality, including odometry deviation and motion blur. Based on the motion characteristics of the robot, we have proposed a dataset creation and selection method to mitigate the impact of these degradations. The effectiveness of this method was verified through experiments with a hardware prototype robot, which demonstrates that it is possible to avoid the influence of matched joint movement deviation by using overlapping simple trajectories; and pre-filtering out blurry images, which usually concentrate on the beginning and stopping periods. Additionally, we conducted a robustness study of mainstream reconstruction methods under limited illumination conditions to quantitively study the performance degradation in a more realistic environment.