  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.

Computer Vision and Pattern Recognition;Robotics