Automated wall‐climbing robot for concrete construction inspection
Automated wall‐climbing robot for concrete construction inspection
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DOI：
10.1002/rob.22119
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发表时间：
2022-09
影响因子：
8.3
通讯作者：
Liang Yang;Bing Li;Jinglun Feng;Guoyong Yang;Yong Chang;Bo Jiang;Jizhong Xiao
中科院分区：
文献类型：
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作者：
Liang Yang;Bing Li;Jinglun Feng;Guoyong Yang;Yong Chang;Bo Jiang;Jizhong Xiao
人造混凝土结构需要先进的检测工具，以确保施工质量符合适用的建筑规范，并保持老化基础设施的可持续性。本文介绍了一种用于公制混凝土检测的爬壁机器人，该机器人可以通过近距离观察到达难以接近的位置，进行视觉数据收集和真实的实时缺陷检测和定位。爬壁机器人能够检测混凝土表面缺陷（即，裂纹和剥落），并生成带有提取的位置线索和度量测量值的缺陷突出显示3D模型。该系统包括四个模块，包括用于捕获RGB‐D帧和惯性测量单元数据的数据收集模块、用于生成姿态耦合关键帧的视觉惯性导航系统模块、用于生成深度神经网络模块的深度神经网络模块。（即InspectionNet）将每个像素分为三个类（背景、裂纹和剥落），以及语义重建模块，用于将每帧测量值集成到全局体积模型中，并突出显示缺陷。我们发现商业RGB‐D相机输出深度是带有孔的噪声，并且引入了用于深度完成的高斯双边滤波器来修补深度图像。该方法实现了最先进的深度完成精度，即使是大孔。基于语义网格，我们引入了一种相干的缺陷度量评估方法来计算裂纹和层裂面积的度量（例如，长度、宽度、面积和深度）。在混凝土桥上的现场实验表明，我们的爬壁机器人能够在粗糙的表面上操作，并且可以跨越浅间隙。该机器人能够在低照度环境和无纹理环境下检测和测量表面缺陷。除了机器人系统，我们还创建了第一个可公开访问的混凝土结构剥落和裂缝数据集，其中包括820个标记图像和超过10，000个现场收集的图像，并将其发布给研究社区。
Human‐made concrete structures require cutting‐edge inspection tools to ensure the quality of the construction to meet the applicable building codes and to maintain the sustainability of the aging infrastructure. This paper introduces a wall‐climbing robot for metric concrete inspection that can reach difficult‐to‐access locations with a close‐up view for visual data collection and real‐time flaws detection and localization. The wall‐climbing robot is able to detect concrete surface flaws (i.e., cracks and spalls) and produce a defect‐highlighted 3D model with extracted location clues and metric measurements. The system encompasses four modules, including a data collection module to capture RGB‐D frames and inertial measurement unit data, a visual–inertial navigation system module to generate pose‐coupled keyframes, a deep neural network module (namely InspectionNet) to classify each pixel into three classes (background, crack, and spall), and a semantic reconstruction module to integrate per‐frame measurement into a global volumetric model with defects highlighted. We found that commercial RGB‐D camera output depth is noisy with holes, and a Gussian‐Bilateral filter for depth completion is introduced to inpaint the depth image. The method achieves the state‐of‐the‐art depth completion accuracy even with large holes. Based on the semantic mesh, we introduce a coherent defect metric evaluation approach to compute the metric measurement of crack and spall area (e.g., length, width, area, and depth). Field experiments on a concrete bridge demonstrate that our wall‐climbing robot is able to operate on a rough surface and can cross over shallow gaps. The robot is capable to detect and measure surface flaws under low illuminated environments and texture‐less environments. Besides the robot system, we create the first publicly accessible concrete structure spalls and cracks data set that includes 820 labeled images and over 10,000 field‐collected images and release it to the research community.