Ground plane fitting algorithm for fast ground removal in LAS/LAZ Point Clouds. A preprocessing step, where ground points removal significantly reduces the number of points involved in proceeding computations.
This project is based on the pseudocode of the ground plane fitting algorithm proposed in the work of Zermas et al. The pseudocode is described below.
P: point cloud
num_iter_: number of iterations
num_lpr_: number of points used to estimate the LPR
th_seeds_: threshold points to be considered initial seeds
th_dist_: threshold distance of the plane
mainLoop(P):
P_ground = extractInitialSeeds(P)
for i in 1 : num_iter_:
model = estimatePlane(P_ground);
clear(P_ground, P_notground)
for point p in PointCloud P:
if model(p) < th_dist_:
add p to P_ground；
else
add p to P_notground;
return P_ground, P_notground
extractInitialSeeds(P):
P_sorted = sortOnHeight(P)
LPR_height = Average(P_sorted(1 : num_lpr_));
clear(P_seeds)
for i in 1 : P_sorted.size:
if P_sorted[i].z < LPR_height + th_seeds_:
add p_sorted[i] to P_seeds
return P_seeds-
Install LAStools:
wget http://lastools.github.io/download/LAStools.zip unzip LAStools.zip cd LAStools; mv LASlib/src/LASlib-config.cmake LASlib/src/laslib-config.cmake mkdir build; cd build cmake .. sudo make install -
Install PCL:
sudo apt install libpcl-dev
-
Download the project.
git clone https://github.com/Amsterdam-AI-Team/3D_Ground_Segmentation.git -
Build the project as follows.
mkdir build cd build cmake .. make -
Run the project.
cd build ./ground_plane_fit
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