IDENTIFYING CORRESPONDING SEGMENTS FROM REPEATED SCAN DATA
- Dept. of Remote Sensing Delft University of Technology Kluyverweg 1, 2629 HS, Delft, NL
Keywords: Change detection, LIDAR, point cloud, segmentation, identification
Abstract. It has been demonstrated that surface changes in the order of millimeters are detectable using terrestrial laser scanning. In practice however, it is still virtually impossible to detect such small changes from for example repeated scans of a complex industrial scene. The three main obstructions are, ﬁrst, a priori uncertainty on what objects are actually changing, second, errors introduced by registration, and third, difﬁculties in the identiﬁcation of identical object parts. In this paper we introduce a method enabling efﬁcient identiﬁcation that can also be applied to evaluate the quality of a registration. The method starts with a pair of co-registered point clouds, at least partially representing the same scene. First, both point clouds are segmented, according to a suited homogeneity criterion. Based on basically orientation and location, corresponding segment parts are identiﬁed, while lack of correspondence leads to the identiﬁcation of either occlusions or large changes. For the corresponding segments, subtle changes at the millimeter level could be analyzed in a next step. An initial version of the new method is demonstrated on repeated scan data of a metro station experiencing heavy construction works.