Change detection and irregular object extraction in 3D point clouds is a
challenging task that is of high importance not only for autonomous navigation
but also for updating existing digital twin models of various industrial
environments. This article proposes an innovative approach for change detection
in 3D point clouds using deep learned place recognition descriptors and
irregular object extraction based on voxel-to-point comparison. The proposed
method first aligns the bi-temporal point clouds using a map-merging algorithm
in order to establish a common coordinate frame. Then, it utilizes deep
learning techniques to extract robust and discriminative features from the 3D
point cloud scans, which are used to detect changes between consecutive point
cloud frames and therefore find the changed areas. Finally, the altered areas
are sampled and compared between the two time instances to extract any
obstructions that caused the area to change. The proposed method was
successfully evaluated in real-world field experiments, where it was able to
detect different types of changes in 3D point clouds, such as object or
muck-pile addition and displacement, showcasing the effectiveness of the
approach. The results of this study demonstrate important implications for
various applications, including safety and security monitoring in construction
sites, mapping and exploration and suggests potential future research
directions in this field.

本文提出一种基于深度学习的点云配准和不规则物体提取的三维点云变化检测方法，并利用地图合并算法建立一个公共的坐标系，通过提取 3D 点云的鲁棒且具有区分性的特征来检测连续点云帧之间的变化并且找到变化区域，最后通过与两个时间点比较来提取任何导致该区域改变的障碍物。该方法在现场实验中得到成功评估，能够检测出 3D 点云中的不同类型的变化，具有构筑物施工安全监控，测绘和勘探等领域的重要应用和发展前景。