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.

翻译：三维点云中的变化检测与不规则物体提取是一项具有挑战性的任务，不仅对自主导航至关重要，而且对更新各种工业环境的现有数字孪生模型也具有重要意义。本文提出了一种创新方法，利用深度学习的地点识别描述符和基于体素到点比较的不规则物体提取技术，实现三维点云的变化检测。该方法首先通过地图合并算法对齐双时态点云，以建立统一的坐标框架。随后，利用深度学习技术从三维点云扫描中提取鲁棒且具有判别力的特征，用于检测连续点云帧之间的变化，从而定位变化区域。最后，对变化区域进行采样，并在两个时间实例之间进行比较，以提取导致区域变化的障碍物。该方法在实际现场实验中成功验证，能够检测三维点云中的不同类型变化，例如物体或堆料添加与位移，展示了该方法的有效性。研究结果对多种应用具有重要启示，包括施工现场的安全监控、地图绘制与勘探，并提出了该领域未来的潜在研究方向。