Abstract
This study presents a point cloud de-noising and calibration approach that takes advantage of point redundancy in both space and time (4D). The purpose is to detect displacements using terrestrial laser scanner data at the sub-mm scale or smaller, similar to radar systems, for the study of very small natural changes, i.e., pre-failure deformation in rock slopes, small-scale failures or talus flux. The algorithm calculates distances using a multi-scale normal distance approach and uses a set of calibration point clouds to remove systematic errors. The median is used to filter distance values for a neighbourhood in space and time to reduce random type errors. The use of space and time neighbours does need to be optimized to the signal being studied, in order to avoid smoothing in either spatial or temporal domains. This is demonstrated in the application of the algorithm to synthetic and experimental case examples. Optimum combinations of space and time neighbours in practical applications can lead to an improvement of an order or two of magnitude in the level of detection for change, which will greatly improve our ability to detect small changes in many disciplines, such as rock slope pre-failure deformation, deformation in civil infrastructure and small-scale geomorphological change.
Highlights
Three-dimensional point clouds used for the analysis of topographic changes have changed the way we assess and manage geohazards and have helped us to understand a variety of geomorphological processes due to their high level of detail and ease of data collection
Point clouds collected from a Terrestrial Laser Scanner (TLS) can be collected efficiently with high accuracy and resolution and can be used to detect small-scale topographic changes [9,15,20]
A controlled experiment was conducted in a laboratory to test the capability of the algorithm to detect very small displacements with real TLS data
Summary
Three-dimensional point clouds used for the analysis of topographic changes have changed the way we assess and manage geohazards and have helped us to understand a variety of geomorphological processes due to their high level of detail and ease of data collection. Point clouds collected from a Terrestrial Laser Scanner (TLS) can be collected efficiently with high accuracy and resolution and can be used to detect small-scale topographic changes [9,15,20]. Compared to change detection with radar systems [7], TLS monitoring has some notable advantages over radar techniques, including its portability, its economy of operation, its ability to detect deformation in directions oblique to the laser line of sight and its higher accuracy point clouds. Data processing is not affected by sudden accelerations [21,22,23,24] This makes TLS advantageous for studying topographic changes in 3D over a variety of study sites in complex geomorphological settings
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