A quality improvement method for 3D laser slam point clouds based on geometric primitives of the scan scene

3D laser simultaneous localization and mapping (SLAM) technology is one of the most efficient methods to capture spatial information. However, the low-accuracy SLAM point cloud limits its application in many fields. To address the problem, a laser SLAM point cloud quality improvement method based on geometric primitive (PCQI-GP) is proposed, which mainly includes extraction of reference datum and point cloud data correction. More specifically, the point cloud data quality is evaluated through extracting the geometric primitives of the scanning scene using the random sampling consistency algorithm, and the high-accuracy point cloud is defined as the reference datum. Furthermore, the primitive parameters extracted from reference datum are adopted to construct constraint conditions, and the coordinates of drifting point cloud data are corrected. Experiments are conducted with data collected by a handheld laser scan system in two challenging scenes to evaluate the PCQI-GP method. Compared with the theoretical design values, the correction accuracy of the PCQI-GP method is less than 3 cm. The experimental results demonstrate that the proposed method can effectively improve the quality of the laser SLAM point cloud in the area without global navigation satellite system (GNSS) signal and sufficient feature points.