Journal
REMOTE SENSING
Volume 14, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/rs14040857
Keywords
mobile laser scanning; point clouds; quality control; georeferencing; systematic errors; transformation parameters; quality enhancement
Categories
Funding
- Austrian Highway Agency
- Graz University of Technology
Ask authors/readers for more resources
This paper proposes an efficient method for quality control of MLS point clouds, which is capable of detecting and correcting small, high-frequency errors. The method is demonstrated to be effective in mitigating systematic errors in real-world data through examples.
The increasing demand for 3D geospatial data is driving the development of new products. Laser scanners are becoming more mobile, affordable, and user-friendly. With the increased number of systems and service providers on the market, the scope of mobile laser scanning (MLS) applications has expanded dramatically in recent years. However, quality control measures are not keeping pace with the flood of data. Evaluating MLS surveys of long corridors with control points is expensive and, as a result, is frequently neglected. However, information on data quality is crucial, particularly for safety-critical tasks in infrastructure engineering. In this paper, we propose an efficient method for the quality control of MLS point clouds. Based on point cloud discrepancies, we estimate the transformation parameters profile-wise. The elegance of the approach lies in its ability to detect and correct small, high-frequency errors. To demonstrate its potential, we apply the method to real-world data collected with two high-end, car-mounted MLSs. The field study revealed tremendous systematic variations of two passes following tunnels, varied co-registration quality of two scanners, and local inhomogeneities due to poor positioning quality. In each case, the method succeeds in mitigating errors and thus in enhancing quality.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available