A new data preprocessing method for 3D reconstruction of pavement

ABSTRACT

Three-dimensional (3D) reconstruction of pavement surface based on the laser scanning can be used to extract the elevation information for evaluating the damage degree of the pavement. This technology is of great significance to road safety as well as the advances of smart transportation. The preprocessing procedures of the laser-scanned point cloud serve as the basis for accurate surface reconstruction, which includes outlier removal, smoothing and sampling. In view of the limitations of the existing preprocessing methods for pavement applications, this paper proposes a new preprocessing method. First, a local density-based outlier removal algorithm is used to solve the problem of poor adaptability or long computation time of the regular outlier removal algorithms. And then, a continuous algorithm based on the moving least squares method is used to smooth and sample the points, solving the problem of repeated degradation of the data. Finally, the field tests on three types of pavements are carried out to verify the feasibility of the proposed preprocessing method. The accuracy of the reconstructed surfaces of the pavements is improved by the new preprocessing method.

KEYWORDS:

• Local density
• moving least squares
• outlier removal
• sampling
• smoothing

Acknowledgement

The authors express their sincere appreciation to the support.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research reported in this paper is supported by the National Key R&D Program of China [grant number 2017YFF0205600].