LiDARSimLib and Placement Evaluation

LiDAR.simulation.and.placement.mp4

LiDAR Simulation Library

Requirements

• Carla (>=0.9.12)

LiDAR List

1. Surround LiDAR
   1. pandar64
   2. ruby128
   3. pandar128
   4. vlp16
   5. hdl64
   6. pandar_qt
   7. bpearl
   8. pandar_40m
   9. pandar_40p
   10. pandar_xt
   11. vlp32
   12. os1_64_gen2
2. Solid State LiDAR
   1. mems_m1
3. Risley Prism LiDAR
   1. horizon
4. Default Carla LiDAR
   1. default LiDARs

Usage

1. There are two paths should be modified:

LiDARSimLib/Unreal/CarlaUE4/Plugins/Carla/Source/Carla/Sensor/livox.cpp
line9: modify depend on your own carla path
LiDARSimLib/PythonAPI/lidar_lib_example/lidar_display.py
line30: modify depend on your own carla path

2. Use this file to overwrite the original carla directory

3. Rebuild carla

make clean

make PythonAPI

make launch

4. Create LiDAR

lidar_bp = world.get_blueprint_library().find('sensor.lidar.ray_cast') # choose ray_cast or ray_cast_semantic

lidar_bp.set_attribute("lidar_type", "Surround") # set lidar_type as Surround, Solid_state, Risley_prism

lidar_bp.set_attribute("name","pandar64") # set name as any one in LiDAR List

5. Set LiDAR ghosting object effect

lidar_bp.set_attribute("enable_ghost", "true") # enable ghosting object effect

6. Motion distortion simulation

• Import the LidarMotonDistortion module

import sys
sys.path.append("/your/path/of/carla/LibCustomFunction") # append the LibCustomFunction
from enable_motion_distortion import LidarMotonDistortion

• Init LidarMotonDistortion with file_path and distortion delay_time

lidar_motion_distort = LidarMotonDistortion("./horizon/", 10) # file_path is where the data you want to save. delay_time is the ratio of simulator frequency to lidar frequency.

• Enable motion distortion

ego_lidar.listen(lambda data: lidar_motion_distort.enable_motion_distortion(data, True))

Results

LiDAR beam simulation

Motion distortion simulation

Ghosting object effect simulation

LiDAR Placement Evaluation in V2X Scenario

We build our evaluation code upon v2x-vit project.

Usage

• Follow the instruction in the readme page in v2x-vit and install the v2x-vit project.

• Download the pretrained model from here.

• Use the script in Placement-Evaluation/v2xvit/tools/inference.py for evaluation.

LiDAR Placement Evaluation in V2X Scenario

We build our evaluation code upon v2x-vit project.

Usage

1. Follow the instruction in the readme page in v2x-vit and install the v2x-vit project.
2. Download the pretrained model from here.
3. Use the script in Placement-Evaluation/v2xvit/tools/inference.py for evaluation.