LiDAR simulation

Simulate LiDAR point cloud from RGBD dataset with various options such as scanning 3D model, Gaussian noise, LiDAR Fov, LiDAR interval angle etc.
This open source tool is a simple program that simulates LiDAR through automatically generates 3D scan data (point cloud). When you need a 3D point cloud but it is difficult to obtain, you can generate scan data from a virtual 3D model(mesh). It can be used for deep learning, etc. Includes 3D model scan, lidar angle, spacing, noise (Gaussian, uniform support) generation options, etc.

description

LiDAR point cloud generation.

input=model_complex.obj, fov=60, interval=20, range=20.0, noise(gaussian)=0.01

Left figure. input=model_complex1.obj, fov=180, interval=100, range=5.0, noise(gaussian)=0.05. Right figure. noise(uniform)=0.05

version history

v0.1

LiDAR point cloud generation draft version.

v0.2

2023.12.5., refactoring. 2023.12.16., noise option(gaussian | uniform) viewer option. test. fixed bug. update.

install

git clone https://github.com/mac999/simulate_LiDAR.git
pip install traceback, tqdm, numpy
pip install trimesh
pip install open3d

run

python simulate_LiDAR.py [options]

--input: default='model.obj', help='input mesh model file(.obj, .ply)'
--output: default='output.pcd', help='output file(.pcd)'
--pos: default='0.0,0.0,0.0', help='LiDAR position'
--yaw: default=0.0, help='LiDAR yaw angle'
--fov: default=60.0, help='LiDAR field of view'
--range: default=10.0, help='LiDAR range'
--noise_option: default='uniform', help='Noise option. [gaussian | uniform]
--noise', default=0.2, help='Noise level. ex) sigma = 0.05 in Gaussian standard deviation or uniform range'
--interval: default=100, help='LiDAR interval count'
--interval_angle: default=0.0, help='LiDAR interval angle'
--viewer: default='on: help='run viewer = [on | off]'

license

MIT license