Dorothy863/Carla-Visual-Relocalization

Generate dataset for visual localiation in CARLA 0.8.2

Carla-Visual-Relocalization

Generate dataset for visual relocalization in CARLA 0.8.2

1 Generate Dataset in Carla

• Find the Two CARLA scripts, then add it to CARLA_0.8.2/PythonClient/scripts/CARLA

  RelocalizationQuery.py

  RelocalizationReference.py

• Start carla simulator in Town01 or Town 02 (default Town01) with PowerShell

  .\CarlaUE4.exe -carla-server -windowed -ResX=400 -ResY=300  -benchmark -fps=10
  .\CarlaUE4.exe  /Game/Maps/Town02  -carla-server -windowed -ResX=400 -ResY=300  -benchmark -fps=10

• Set up weather, playerstart, vehicles, pedestrians etc. then run RelocalizationReference.py

• Set up weather, playerstart, vehicles, pedestrians etc. then run RelocalizationQuery.py

• Output format

folder name	description
e.g. W000_P100_V000_P000	weather, playerstart, vehicles, pedestrians
W000_P100_V050_P200	as above
W000_P100_V075_P300	as above

subfolder name	description
/Depth	key frame,256*256
/RGB	key frame,256*256
/SemanticSegmentation	key frame,256*256
Control.txt	each frame,256*256
Trajectory.txt	each frame,256*256

2 Use Dynamic2static Model to infer

• Run CarlaProcessed.ipynb

• Output format

folder name	description
/AB	dynamic,static, width=256*2, height=256
/ABC	dynamic,static,segmantic mask, width=256*3, height

• Run test_with_mask to infer, then download to local pc from remote server

pretrained Dynamic2static model

• Output format

folder name	description
/images	fake_B, Gx, real_A,....

• Run images2processed.ipynb

deltete Gx, real_A,..., rename fake_B.png to xxxxxx.png

3 Evaluate Precision of Visual ReLocalization

• In pycharm run ImageRetrieval.py

• in jupyter notebook run get_precision.ipynb (recommended)