3D Point Cloud Generation Based on Multi-Sensor Fusion

Abstract

Traditional precise engineering surveys adopt manual static, discrete observation, which cannot meet the dynamic, continuous, high-precision and holographic fine measurements required for large-scale infrastructure construction, operation and maintenance, where mobile laser scanning technology is becoming popular. However, in environments without GNSS signals, it is difficult to use mobile laser scanning technology to obtain 3D data. We fused a scanner with an inertial navigation system, odometer and inclinometer to establish and track mobile laser measurement systems. The control point constraints and Rauch-Tung-Striebel filter smoothing were fused, and a 3D point cloud generation method based on multi-sensor fusion was proposed. We verified the method based on the experimental data; the average deviation of positioning errors in the horizontal and elevation directions were 0.04 m and 0.037 m, respectively. Compared with the stop-and-go mode of the Amberg GRP series trolley, this method greatly improved scanning efficiency; compared with the method of generating a point cloud in an absolute coordinate system based on tunnel design data conversion, this method improved data accuracy. It effectively avoided the deformation of the tunnel, the sharp increase of errors and more accurately and quickly processed the tunnel point cloud data. This method provided better data support for subsequent tunnel analysis such as 3D display, as-built surveying and disease system management of rail transit tunnels.

Deformation Detection of Mining Tunnel Based on Automatic Target Recognition

“…The positioning accuracy of this method was 5.51 cm in the horizontal direction and 2.74 cm in the vertical direction. Another study [33] proposed a 3D point cloud generation method based on multi-sensor fusion. The positioning accuracy of this method was 4.0 cm in the horizontal direction and 3.7 cm in the vertical direction.…”

“…It is difficult to rely solely on the mileage positioning in the mobile tunnel laser detection system, and the poor mileage positioning accuracy makes it difficult to unify the multiphase data benchmark. Han et al [32,33] realized mining tunnel positioning by using the linear recovery of relative point clouds based on the measured track centerline of the tunnel. In addition, they proposed a 3D point cloud generation method based on multi-sensor fusion, which uses a few targets to constrain inertial navigation and odometers, and fuses scanners to generate the 3D point cloud data.…”

Experiment and Application of NATM Tunnel Deformation Monitoring Based on 3D Laser Scanning

“…Based on mobile laser scanning technology, many scholars at home and abroad have designed building information models (BIMs) to develop deformation monitoring systems for tunnels or other retaining structures [10,11]. At the same time, 3D laser scanning technology can not only design the structural positioning and orientation system [12][13][14][15][16][17] but also create a 3D building information model (BIM) [18,19] based on the orientation system. In fact, by optimizing the deformation data extraction and improving the registration algorithm [20][21][22][23], the accuracy of tunnel 3D modeling and deformation monitoring can be efectively improved [14,[24][25][26].…”

Event-Triggered Robust Fusion Estimation for Multi-Sensor Time-Delay Systems with Packet Drops

“…In the last decade, sensor systems have been extensively studied in path planning [1], environmental monitoring [2], motor control [3], and trajectory tracking [4,5], and so on. In multi-sensor systems, the accuracy and stability of the system are improved due to the joint data collection by multiple sensors.…”