TY  - JOUR
PY  - 2019//
TI  - Automatic ground points identification method for roadside LiDAR data
JO  - Transportation research record
A1  - Wu, Jianqing
A1  - Xu, Hao
A1  - Lv, Bin
A1  - Yue, Rui
A1  - Li, Yang
SP  - 140
EP  - 152
VL  - 2673
IS  - 6
N2  - Roadside light detection and ranging (LiDAR) provides a solution to fill the data gap under mixed traffic situations. The real-time high-resolution micro traffic data (HRMTD) of all road users from the roadside LiDAR sensor provides a new opportunity to serve the connected-vehicle system during the transition period from unconnected vehicles to connected vehicles. Ground surface identification is the basic data processing step for HRMTD collection. The current ground points identification algorithms based on airborne and mobile LiDAR do not work for roadside LiDAR. A novel algorithm is developed in this paper to identify and exclude ground points based on the features of LiDAR, terrain, and point density in the space. The scan feature of different beams is used to search ground points. The whole procedure can be divided into four major parts: points clustering in each beam, slope-based filtering, shape-based filtering, and ground points matrix extraction. The proposed algorithm was evaluated using the real-world LiDAR data collected at different scenarios. The results showed that this algorithm can be used for ground points exclusion under different situations (differing terrain types, weather situations, and traffic volumes) with high accuracy. This algorithm was compared with previously developed algorithms. The overall performance of the proposed algorithm is superior. The low computational load guarantees this method may be applied for real-time data processing.<p />  <p>Language: en</p>
LA  - en
SN  - 0361-1981
UR  - http://dx.doi.org/10.1177/0361198119843869
ID  - ref1
ER  -