Fully Automatic Large-Scale Point Cloud Mapping for Low-Speed Self-Driving Vehicles in Unstructured Environments

Author(s):  
Xiang Gao ◽  
Qi Wang ◽  
Hao Gu ◽  
Fang Zhang ◽  
Guoqi Peng ◽  
...  
Author(s):  
L. Gézero ◽  
C. Antunes

In the last few years, LiDAR sensors installed in terrestrial vehicles have been revealed as an efficient method to collect very dense 3D georeferenced information. The possibility of creating very dense point clouds representing the surface surrounding the sensor, at a given moment, in a very fast, detailed and easy way, shows the potential of this technology to be used for cartography and digital terrain models production in large scale. However, there are still some limitations associated with the use of this technology. When several acquisitions of the same area with the same device, are made, differences between the clouds can be observed. The range of that differences can go from few centimetres to some several tens of centimetres, mainly in urban and high vegetation areas where the occultation of the GNSS system introduces a degradation of the georeferenced trajectory. Along this article a different method point cloud registration is proposed. In addition to the efficiency and speed of execution, the main advantages of the method are related to the fact that the adjustment is continuously made over the trajectory, based on the GPS time. The process is fully automatic and only information recorded in the standard LAS files is used, without the need for any auxiliary information, in particular regarding the trajectory.


2021 ◽  
Vol 182 ◽  
pp. 37-51
Author(s):  
Jing Du ◽  
Guorong Cai ◽  
Zongyue Wang ◽  
Shangfeng Huang ◽  
Jinhe Su ◽  
...  

2013 ◽  
Vol 19 (10) ◽  
pp. 1700-1707 ◽  
Author(s):  
Yong-Jin Liu ◽  
Jun-Bin Zhang ◽  
Ji-Chun Hou ◽  
Ji-Cheng Ren ◽  
Wei-Qing Tang

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