A 4D Filtering and Calibration Technique for Small-Scale Point Cloud Change Detection with a Terrestrial Laser Scanner

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 OPEN ACCESS Remote Sens. 2015, 7 13030 in many disciplines, such as rock slope pre-failure deformation, deformation in civil infrastructure and small-scale geomorphological change.

Image-based surface reconstruction in geomorphometry – merits, limits and developments

“…By point cloud interpolation alone (point-to-mesh), this issue is not solvable because there are still problems at very rough surfaces (Lague et al, 2013). Different solutions have been proposed: on the one hand, Abellán et al (2009) proposed averaging the point cloud difference along the spatial dimension, which can also be extended to 4-D (x, y, z, time; Kromer et al, 2015). On the other hand, Lague et al (2013) proposed the M3C2 algorithm for point cloud comparison that considers the local roughness and further computes the statistical significance of detected changes.…”

Optimising 4-D surface change detection: an approach for capturing rockfall magnitude–frequency

“…The collection of a high-frequency time series of scan data presents the opportunity to reduce uncertainty by averaging point positions through both time and space, as points are independent in neither space nor in time. This averaging can take the form of averaging the 3-D position of each point, as utilised here and in M3C2 (Lague et al, 2013), or the averaging of differences between points (Abellán et al, 2009;Kromer et al, 2015b).…”

“…Our second aim, therefore, is to minimise the errors that arise from near-continuous monitoring in order to reduce the minimum detectable movement. Kromer et al (2015b) presented a 4-D smoothing technique to reduce the offset between successive point clouds, such as those from near-continuous monitoring. Similarly, the method presented here is optimised for handling large (10 2 -10 4 ) numbers of high-resolution 3-D scans, critically without user intervention, but can also be applied to point clouds from non-continuous monitoring.…”

Using street view imagery for 3-D survey of rock slope failures

“…and its evolution over time by comparing 3-D point clouds acquired at different time steps. For example, 3-D remote sensing techniques are helping to better quantify key aspects of rock slope evolution, including the accurate quantification of rockfall rates and the deformation of rock slopes before failure using both lidar (Rosser et al, 2005;Oppikofer et al, 2009;Royan et al, 2014;Kromer et al, 2015;Fey and Wichmann., 2017) and photogrammetrically derived point clouds (Walstra et al, 2007;Lucieer et al, 2013, Stumpf et al, 2015Fernandes et al, 2016;Guerin et al, 2017;Ruggles et al, 2016).…”