3D Point Cloud Generation Based on Multi-Sensor Fusion

被引:4
|
作者
Han, Yulong [1 ]
Sun, Haili [1 ]
Lu, Yue [1 ]
Zhong, Ruofei [1 ]
Ji, Changqi [1 ]
Xie, Si [2 ]
机构
[1] Capital Normal Univ, Acad Multidisciplinary Studies,Beijing Adv Innova, Coll Resource Environm & Tourism, Key Lab 3D Informat Acquisit & Applicat,MOE, Beijing 100048, Peoples R China
[2] State Key Lab Rail Transit Engn Informatizat FSDI, Xian 710043, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 19期
基金
中国国家自然科学基金;
关键词
mobile laser scanning; 3D point cloud; inertial navigation; RTS smoothing; recursive average filter;
D O I
10.3390/app12199433
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
收藏
页数:26
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