Abstract
Abstract. The chances of acquiring three-dimensional (3D) point clouds have recently increased with the emergence of laser scanners. Hence, 3D monitoring of various objects through the accumulation of “time-series 3D point clouds,” which are point clouds of the same place at different times, is possible. Change detection is a task that is indispensable in 3D monitoring. One of the most common change detection method of 3D point clouds is simple subtraction between two data. However, this method is vulnerable to various errors. Therefore, change detection methods that are robust to errors are required. In this study, we developed robust principal component analysis, which has become popular in the background modelling of video images, to robustly recognize changes in time-series 3D point clouds. We first applied the proposed method to time-series depth images and confirmed its accuracy. We then applied the method to the digital elevation models of Mt. Unzen, which were acquired between 2003 and 2016, to recognize yearly elevation changes. The results show that the proposed method robustly recognizes elevation changes with a properly set parameter.
Highlights
The chances of acquiring three-dimensional (3D) point clouds have recently increased with the emergence of laser scanners
Some unchanged pixels were detected as changes, which can be attributed to measurement errors in the changed depth image
We proposed a method to detect changes in timeseries 3D point clouds for the future object monitoring through laser scanning
Summary
The chances of acquiring three-dimensional (3D) point clouds have recently increased with the emergence of laser scanners. Change detection is a task that is indispensable in 3D monitoring (Sun et al, 2015; Tran et al, 2018; Xiao et al, 2015). One of the most common change detection method of 3D point clouds is simple subtraction between two data. This method is vulnerable to various errors. Errors are caused by the colour and material of objects, IMU and GNSS on platforms, time resolution of laser scanners, and overlaying of multiple data. Change detection methods that are robust to errors are required
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have