This article focuses on a novel three-dimensional reconstruction system that maps large archeological caves using data collected by a small unmanned aircraft system with red, green, and blue-depth cameras. Cave sites often contain the best-preserved material in the archeological record. Yet few sites are fully mapped. Large caves environment usually contains complex geometric structures and objects, which must be scanned with long overlapped camera trajectories for better coverage. Due to the error in camera tracking of such scanning, reconstruction results often contain flaws and mismatches. To solve this problem, we propose a framework for surface loop closure, where loops are detected with a compute unified device architecture accelerated point cloud registration algorithm. After a loop is detected, a novel surface loop filtering method is proposed for robust loop optimization. This loop filtering method is robust to different scan patterns and can cope with tracking failure recovery so that there is more flexibility for unmanned aerial vehicles to fly and record data. We run experiments on public data sets and our cave data set for analysis and robustness tests. Experiments show that our system produces improved results on baseline methods.

中文翻译：

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使用小型无人机系统和红色、绿色和蓝色深度相机对无约束洞穴扫描进行智能 3D 处理

本文重点介绍一种新颖的 3D 重建系统，该系统使用由带有红色、绿色和蓝色深度相机的小型无人机系统收集的数据绘制大型考古洞穴地图。洞穴遗址通常包含考古记录中保存最完好的材料。然而，很少有站点被完全映射。大型洞穴环境通常包含复杂的几何结构和物体，必须使用长重叠的相机轨迹进行扫描以获得更好的覆盖范围。由于这种扫描的相机跟踪误差，重建结果通常包含缺陷和不匹配。为了解决这个问题，我们提出了一个表面闭环框架，其中使用计算统一设备架构加速点云配准算法检测循环。检测到循环后，提出了一种新的表面环路滤波方法，用于鲁棒环路优化。这种环路滤波方法对不同的扫描模式具有鲁棒性，并且可以应对跟踪故障恢复，从而使无人机飞行和记录数据具有更大的灵活性。我们对公共数据集和我们的洞穴数据集进行实验，以进行分析和稳健性测试。实验表明，我们的系统在基线方法上产生了改进的结果。