93 / 2023-05-24 15:05:24

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

Underground mines; point cloud; SLAM; geometric primitives

High-resolution and high-accuracy 3D spatial information collection play an important role in many applications such as building information modeling (BIM) construction, deformation monitoring, indoor navigation, and underground space mapping. However, traditional survey methods based on terrestrial laser scanning (TLS) technology are time-consuming and labor-intensive, requiring measurements to be taken at a sequence of static stations. In recent years, simultaneous localization and mapping (SLAM) technology in the field of robotics provides a faster and easier solution, making it possible to construct the incremental map in an unknown environment. In addition, the real-time location of the scanning platform could be determined after establishing the scene map, which has become one of the most efficient methods to capture spatial information. However, it is difficult to make full use of redundant observations to optimize position information due to the real-time requirements of SLAM positioning in underground space, leading to internal inconsistency in the point cloud and affecting subsequent processing.

To address the problem, a laser SLAM point cloud quality improvement method based on geometric primitive (PCQI-GP) is proposed in our study, which mainly includes extraction of reference datum and point cloud data correction. More specifically, the point cloud data quality is evaluated by extracting the geometric primitives of the scanning scene using the random sampling consistency (RANSAC) algorithm, such as the normal vector of the plane, the axis vector of the cylinder, and the high-accuracy point cloud data captured when the global navigation satellite system (GNSS) and inertial measurement unit (IMU) can provide accurate location and attitude information is determined through the extracted geometric primitives, which is defined as the reference datum. Furthermore, the primitive parameters extracted from the reference datum are adopted to construct constraint conditions, and the coordinates of drifting point cloud data are corrected.

To evaluate the point cloud quality improvement method based on geometric primitive, the tunnel and mine shaft point cloud, which are scanned by the ZEB-REVO handheld laser scanner, is adopted. The HLS provides a simple and rapid way to capture spatial data through the time-of-flight laser range scanner that is coupled rigidly to an IMU mounted on a motor drive. 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 a global navigation satellite system signal and sufficient feature points.