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Dev #1

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175 changes: 175 additions & 0 deletions .gitignore
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.idea/

# logs
logs*
test.py

# data
data/
results/


### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
.pybuilder/
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version

# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

# poetry
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# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock

# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml

# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/

# pytype static type analyzer
.pytype/

# Cython debug symbols
cython_debug/

# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/

# End of https://www.toptal.com/developers/gitignore/api/python
nn
63 changes: 63 additions & 0 deletions PREPARE_DATA.md
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## IPBLab dataset
Downloading IPBLab dataset from our server:
```shell
cd ir-mcl && mkdir data && cd data
wget https://www.ipb.uni-bonn.de/html/projects/kuang2023ral/ipblab.zip
unzip ipblab.zip
```

For each sequence, we provide :
- seq_{id}.bag: the ROS bag format, include raw odometer reading and raw lidar reading.
- seq_{id}.json: include raw odometer reading, ground-truth poses, and raw lidar reading.
- seq_{id}_gt_pose: the ground-truth poses in TUM format (for evaluation with evo).

Besides, there are also some configuration files are provided:
- lidar_info.json: the parameters of the 2D LiDAR sensor.
- occmap.npy: the pre-built occupancy grid map.
- b2l.txt: the transformation from the lidar link to robot's base link

The final data structure should look like
```
data/
├── ipblab/
│ ├── loc_test/
│ │ ├── test1/
│ │ │ ├──seq_1.bag
│ │ │ ├──seq_1.json
│ │ │ ├──seq_1_gt_pose.txt
│ │ ├──b2l.txt
│ ├──lidar_info.json
│ ├──occmap.npy
```

There is one sequence available for the localization experiments now, the full dataset will be released after our dataset paper is published!

## Intel Lab datatse, Freiburg Building 079 dataset, and MIT CSAIL dataset
Downloading these three classical indoor 2D SLAM datasets from our server:
```shell
cd ir-mcl && mkdir data && cd data
wget https://www.ipb.uni-bonn.de/html/projects/kuang2023ral/2dslam.zip
unzip 2dslam.zip
```

For each sequence, we provide :
- train.json: the training set which is used for mapping or train the NOF model.
- val.json: the validation set for evaluating the model during training.
- test.json: the test set for evaluating the final model.
- occmap.npy: the pre-built occupancy grid map by using training set.

The final data structure should look like
```
data/
├── fr079/
│ ├──occmap.npy
│ ├──train.json
│ ├──val.json
│ ├──test.json
├── intel/
│ ├──...
├── mit/
│ ├──...
```

Here, we provide the converted format of these dataset for ease of use. The raw data could be found on our website: [2D Laser Dataset](https://www.ipb.uni-bonn.de/datasets/).
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