Point-based and plane-based deformation monitoring of indoor environments using terrestrial laser scanners

Abstract

Terrestrial laser scanners are high-accuracy 3D imaging instruments that are capable of measuring deformations with sub-millimetre level accuracy in most close-range applications. Traditionally, deformation monitoring via laser scanning is performed by measuring distinct signalised targets. In this case, the centroid of these targets must be determined with great accuracy for optimum detectability. To achieve this, a least-squares target centroid extraction algorithm suitable for planar checkerboard-type targets is proposed for irregularly organised laser scanner data. These target centroids are then used in a free-station network adjustment for performing deformation analysis with no a priori assumptions about the deformation pattern. To ensure the optimum measurement accuracy, all systematic errors inherent to the instrument at the time of data acquisition needs to be removed. One of the methods for reducing these systematic errors is by performing self-calibration of terrestrial laser scanners. In this paper, this was performed on-site to model the systematic errors of the scanner. It is demonstrated that the accuracy of the recovered translational movements were improved by an order of magnitude from the millimetre level to the sub-millimetre level using this approach. Despite the success of using laser scanners with signalised targets in deformation analysis, the main benefit of active sensors like terrestrial laser scanning systems is their ability to capture 3D information of the entire scene without installing markers. A new markerless deformation analysis technique that utilises intersection points derived from planar-features is proposed and tested in this paper. The extraction and intersection of planes in each point cloud can be performed semi-automatically or automatically. This new method is based on free-stationing and does not require a priori knowledge about stable control points or movement patterns. It can detect and measure both translational and rotational movements of the planes with minimum human interaction. The effectiveness of the proposed methodology is studied through simulations and real datasets captured with a phase-based Leica HDS6100 scanner.