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首页> 外文期刊>Computer-Aided Civil and Infrastructure Engineering >Vision-Based Hierarchical Recognition for Dismantling Robot Applied to Interior Renewal of Buildings
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Vision-Based Hierarchical Recognition for Dismantling Robot Applied to Interior Renewal of Buildings

机译：基于视觉的分层识别机器人在建筑物内部更新中的应用

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New robotic systems are going to play an essential role in the future dismantling service for renewing office interiors in buildings. In dismantling tasks, robots are expected to be able to find and remove very small parts such as screws and bolts. Such recognition of small parts is difficult for robots. The article describes a vision-based hierarchical recognition applied to dismantling tasks where large structures are detected at first, thus small parts attached to these structures are detected easier. Regarding the items in the ceiling side, after the dismantling task of the ceiling panels, it is necessary to remove carefully the screws that once held these panels to the light gauge steel (LGS), with the purpose of reusing it. With the pose detection of the large structure (LGS) and considering a robot arm with a stereo camera on its tip, a trajectory near that structure can be computed to detect the small parts, in this case the screws. The large structure is detected by using a process of line detection in 2D and its 3D pose is measured with the stereo camera. During the motion along the structure, the screws are detected by applying a multi-template matching process to every captured image. Followed by, the Support Vector Machine (SVM), which recognizes those screw candidates with high true positive rate and low false positive one. These rates are improved with a temporal multi-image integration for tracking the screw candidates. In the experiment, 10 actual screws distributed in 1.1 m along a linear segment on the LGS are successfully recognized with a few false positives and with a final computed 3D position of 2 mm in average. Feasibility of methodology is evaluated by experimentation under different lighting conditions in a realistic environment. Experimental results show that the method works well to be applied in an actual dismantling site.
机译：新的机器人系统将在未来的拆除服务中为建筑物的办公室内部更新发挥重要作用。在拆卸任务中，机器人有望能够找到并卸下很小的零件，例如螺钉和螺栓。机器人很难识别这种小零件。本文介绍了一种基于视觉的分层识别方法，该方法应用于拆卸任务时，首先要检测大型结构，因此更容易检测到附着在这些结构上的小零件。关于吊顶侧的物品，在拆除吊顶板之后，有必要小心地卸下将这些吊板固定到轻钢（LGS）上的螺钉，以便重新使用。通过大型结构（LGS）的姿态检测，并考虑在其尖端上装有立体摄像头的机械臂，可以计算出该结构附近的轨迹以检测小零件，在这种情况下为螺钉。通过使用2D线检测过程检测大型结构，并使用立体相机测量其3D姿态。在沿着结构的运动过程中，通过对每个捕获的图像应用多模板匹配过程来检测螺钉。其次，是支持向量机（SVM），它可以识别那些具有高真阳性率和低假阳性率的候选螺钉。这些速度通过用于跟踪螺丝候选的时间多图像集成得到了改善。在实验中，成功识别出沿LGS的线性段分布在1.1 m中的10个实际螺钉，并带有一些假阳性，最终计算出的平均3D位置平均为2 mm。通过在现实环境中的不同照明条件下进行实验来评估方法论的可行性。实验结果表明，该方法可以很好地应用于实际拆除现场。

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    S. Rolando Cruz-Ramfrez; Yasushi Mae; Tatsuo Arai; Tomohito Takubo; Kenichi Ohara;

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    Department of Systems Innovation, Graduate School of Engineering Science, Osaka University,1-3 Machikaneyama, Toyonaka, Osaka, Japan;

    Department of Systems Innovation, Graduate School of Engineering Science, Osaka University,1-3 Machikaneyama, Toyonaka, Osaka, Japan;

    Department of Systems Innovation, Graduate School of Engineering Science, Osaka University,1-3 Machikaneyama, Toyonaka, Osaka, Japan;

    Department of Systems Innovation, Graduate School of Engineering Science, Osaka University,1-3 Machikaneyama, Toyonaka, Osaka, Japan;

    Department of Systems Innovation, Graduate School of Engineering Science, Osaka University,1-3 Machikaneyama, Toyonaka, Osaka, Japan;

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