Change Detection in Laser-Scanned Data of Industrial Sites

As laser scanners become widely used in 3D data acquisition of industrial sites, one challenging problem emerges: given two data of the same site scanned/modeled at different times, how can we tell the difference between the two? In this paper, we formulate this problem as the 3D change detection problem, and propose a novel method for detecting object-level changes. In general, we notice that the changes can be viewed as the inconsistency between the global alignment and the local alignment. Therefore, we propose a change detection framework that comprises global alignment, local object detection and a novel change detection method. Specifically, we propose a series of change evaluation functions for pair wise change inference, based on which we formulate the many-to-many object change correlation problem as the weighted bipartite matching problem which could be solved efficiently. Finally, we demonstrate the feasibility of our approach through experiments on both synthetic and real industrial datasets.