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.

中文翻译：

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用于基于视觉的重建的线驱动柔性机械手的数据集创建和选择方法

为了提高福岛核电站2号反应堆未来调查的空间意识，首先需要通过重建获得环境模型。为了为此任务收集图像，我们开发了一种灵活、紧凑、轻便的机械手，称为 Bundled Wire Drive 机器人。然而，由于机制的缺陷，使用该机器人的可行性受到图像质量潜在下降的限制，包括里程计偏差和运动模糊。基于机器人的运动特性，我们提出了一种数据集创建和选择方法来减轻这些退化的影响。通过硬件原型机器人实验验证了该方法的有效性，这表明可以通过使用重叠的简单轨迹来避免匹配关节运动偏差的影响；并预先过滤掉通常集中在开始和停止期间的模糊图像。此外，我们对有限光照条件下的主流重建方法进行了鲁棒性研究，以定量研究更现实环境中的性能退化。