From c189b44bc55be1b61192019c254dd49608470565 Mon Sep 17 00:00:00 2001 From: KuangHaofei Date: Fri, 3 Feb 2023 09:33:46 +0100 Subject: [PATCH 1/3] init code release --- .gitignore | 175 +++++++++++ PREPARE_DATA.md | 63 ++++ README.md | 146 ++++++++- config/fr079_nof_weights.ckpt | Bin 0 -> 5907839 bytes .../global_localization/loc_config_test1.yml | 65 ++++ .../global_localization/loc_config_test2.yml | 67 +++++ .../global_localization/loc_config_test3.yml | 67 +++++ .../global_localization/loc_config_test4.yml | 67 +++++ .../global_localization/loc_config_test5.yml | 67 +++++ config/intel_nof_weights.ckpt | Bin 0 -> 5907775 bytes config/ipblab_nof_weights.ckpt | Bin 0 -> 5895043 bytes config/mit_nof_weights.ckpt | Bin 0 -> 5907775 bytes config/pose_tracking/loc_config_test1.yml | 59 ++++ config/pose_tracking/loc_config_test2.yml | 59 ++++ config/pose_tracking/loc_config_test3.yml | 59 ++++ config/pose_tracking/loc_config_test4.yml | 59 ++++ config/pose_tracking/loc_config_test5.yml | 59 ++++ environment.yml | 131 ++++++++ eval.py | 131 ++++++++ loc_demo.py | 56 ++++ main.py | 171 +++++++++++ mcl/CMakeLists.txt | 13 + mcl/Makefile | 2 + mcl/initialization.py | 53 ++++ mcl/motion_model.py | 183 ++++++++++++ mcl/rendering.py | 176 +++++++++++ mcl/sensor_model.py | 78 +++++ mcl/setup.py | 85 ++++++ mcl/srrg_utils/pf_library/pf_utils.py | 18 ++ .../pf_library/pybind/CMakeLists.txt | 4 + .../pf_library/pybind/pf_library.cpp | 74 +++++ mcl/srrg_utils/pf_library/pybind/pf_library.h | 26 ++ .../pf_library/pybind/pf_library_pybind.cpp | 29 ++ .../pf_library/pybind/stl_vector_eigen.h | 105 +++++++ mcl/srrg_utils/test_srrg.py | 13 + mcl/vis_loc_result.py | 178 +++++++++++ mcl/visualizer.py | 282 ++++++++++++++++++ nof/__init__.py | 0 nof/criteria/__init__.py | 8 + nof/criteria/loss.py | 47 +++ nof/criteria/metrics.py | 30 ++ nof/criteria/pointcloud_metrics.py | 44 +++ nof/dataset/__init__.py | 6 + nof/dataset/ipb2dmapping.py | 128 ++++++++ nof/dataset/ray_utils.py | 58 ++++ nof/networks/__init__.py | 1 + nof/networks/models.py | 123 ++++++++ nof/nof_utils.py | 134 +++++++++ nof/render.py | 194 ++++++++++++ shells/loc_demo.sh | 6 + shells/pretraining/fr079.sh | 13 + shells/pretraining/intel.sh | 14 + shells/pretraining/ipblab.sh | 13 + shells/pretraining/mit.sh | 13 + train.py | 159 ++++++++++ utils.py | 236 +++++++++++++++ 56 files changed, 4036 insertions(+), 11 deletions(-) create mode 100644 .gitignore create mode 100644 PREPARE_DATA.md create mode 100644 config/fr079_nof_weights.ckpt create mode 100644 config/global_localization/loc_config_test1.yml create mode 100644 config/global_localization/loc_config_test2.yml create mode 100644 config/global_localization/loc_config_test3.yml create mode 100644 config/global_localization/loc_config_test4.yml create mode 100644 config/global_localization/loc_config_test5.yml create mode 100644 config/intel_nof_weights.ckpt create mode 100644 config/ipblab_nof_weights.ckpt create mode 100644 config/mit_nof_weights.ckpt create mode 100644 config/pose_tracking/loc_config_test1.yml create mode 100644 config/pose_tracking/loc_config_test2.yml create mode 100644 config/pose_tracking/loc_config_test3.yml create mode 100644 config/pose_tracking/loc_config_test4.yml create mode 100644 config/pose_tracking/loc_config_test5.yml create mode 100644 environment.yml create mode 100644 eval.py create mode 100644 loc_demo.py create mode 100644 main.py create mode 100644 mcl/CMakeLists.txt create mode 100644 mcl/Makefile create mode 100644 mcl/initialization.py create mode 100644 mcl/motion_model.py create mode 100644 mcl/rendering.py create mode 100644 mcl/sensor_model.py create mode 100644 mcl/setup.py create mode 100644 mcl/srrg_utils/pf_library/pf_utils.py create mode 100644 mcl/srrg_utils/pf_library/pybind/CMakeLists.txt create mode 100644 mcl/srrg_utils/pf_library/pybind/pf_library.cpp create mode 100644 mcl/srrg_utils/pf_library/pybind/pf_library.h create mode 100644 mcl/srrg_utils/pf_library/pybind/pf_library_pybind.cpp create mode 100644 mcl/srrg_utils/pf_library/pybind/stl_vector_eigen.h create mode 100644 mcl/srrg_utils/test_srrg.py create mode 100644 mcl/vis_loc_result.py create mode 100644 mcl/visualizer.py create mode 100644 nof/__init__.py create mode 100644 nof/criteria/__init__.py create mode 100644 nof/criteria/loss.py create mode 100644 nof/criteria/metrics.py create mode 100644 nof/criteria/pointcloud_metrics.py create mode 100644 nof/dataset/__init__.py create mode 100644 nof/dataset/ipb2dmapping.py create mode 100644 nof/dataset/ray_utils.py create mode 100644 nof/networks/__init__.py create mode 100644 nof/networks/models.py create mode 100644 nof/nof_utils.py create mode 100644 nof/render.py create mode 100644 shells/loc_demo.sh create mode 100644 shells/pretraining/fr079.sh create mode 100755 shells/pretraining/intel.sh create mode 100644 shells/pretraining/ipblab.sh create mode 100644 shells/pretraining/mit.sh create mode 100644 train.py create mode 100644 utils.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..e65e8c4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,175 @@ +.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 +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# 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 \ No newline at end of file diff --git a/PREPARE_DATA.md b/PREPARE_DATA.md new file mode 100644 index 0000000..469358d --- /dev/null +++ b/PREPARE_DATA.md @@ -0,0 +1,63 @@ +## IPBLab dataset +Downloading IPBLab dataset from our server: +```shell +cd ir-mcl && mkdir 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 +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/). \ No newline at end of file diff --git a/README.md b/README.md index 4a975b7..22f0a74 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,24 @@ -# IR-MCL: Implicit Representation-Based Online Global Localization -This repo contains the code of the paper: -*IR-MCL: Implicit Representation-Based Online Global Localization*, - -by [Haofei Kuang](https://www.ipb.uni-bonn.de/people/haofei-kuang/), [Xieyuanli Chen](https://www.ipb.uni-bonn.de/people/xieyuanli-chen/), [Tiziano Guadagnino](https://phd.uniroma1.it/web/TIZIANO-GUADAGNINO_nP1536210_IT.aspx), [Nicky Zimmerman](https://www.ipb.uni-bonn.de/people/nicky-zimmerman/), [Jens Behley](https://www.ipb.uni-bonn.de/people/jens-behley/) and [Cyrill Stachniss](https://www.ipb.uni-bonn.de/people/cyrill-stachniss/) +

+

IR-MCL: Implicit Representation-Based Online Global Localization

+

+ Haofei Kuang + · + Xieyuanli Chen + · + Tiziano Guadagnino + · + Nicky Zimmerman + · + Jens Behley + · + Cyrill Stachniss +

+

University of Bonn + +

+

-[Arxiv Preprint](https://arxiv.org/abs/2210.03113) ----- -### IR-MCL pipeline

@@ -21,9 +32,122 @@ by [Haofei Kuang](https://www.ipb.uni-bonn.de/people/haofei-kuang/), [Xieyuanli ## Abstract Determining the state of a mobile robot is an essential building block of robot navigation systems. In this paper, we address the problem of estimating the robot’s pose in an indoor environment using 2D LiDAR data and investigate how modern environment models can improve gold standard Monte-Carlo localization (MCL) systems. We propose a neural occupancy field (NOF) to implicitly represent the scene using a neural network. With the pretrained network, we can synthesize 2D LiDAR scans for an arbitrary robot pose through volume rendering. Based on the implicit representation, we can obtain the similarity between a synthesized and actual scan as an observation model and integrate it into an MCL system to perform accurate localization. We evaluate our approach on five sequences of a self-recorded dataset and three publicly available datasets. We show that we can accurately and efficiently localize a robot using our approach surpassing the localization performance of state-of-the-art methods. The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization. -## Code -Coming soon! + +## Dependencies +The code was tested with Ubuntu 20.04 with: +- python version **3.9**. +- pytorch version **1.13.1** with **CUDA 11.6** +- pytorch-lighting with **1.9.0** + +### Installation +- Clone the repo: + ```shell + git clone https://github.com/PRBonn/ir-mcl.git + cd ir-mcl + ``` + +- Prepare the python environment (Anaconda is recommended here): + ```shell + conda env create -f environment.yml + ``` + or + ```shell + conda create --name irmcl python=3.9.13 + conda activate irmcl + + conda install -c conda-forge pybind11 + pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu116 + pip install pytorch-lightning + pip install matplotlib scipy open3d + pip install evo --upgrade --no-binary evo + ``` + +- Compile the motion model and resampling module + ```shell + cd ir-mcl/mcl & conda activate ir-mcl + make -j4 + ``` + +## Preparation + +### Datasets +Please refer to [PREPARE_DATA](PREPARE_DATA.md) to prepare the datasets + +### Pre-trained Weights + +The pre-trained weights are stored at `config` folder, includes: +- IPBLab dataset: `config/ipblab_nof_weights.ckpt` +- Freiburg Building 079 dataset: `config/fr079_nof_weights.ckpt` +- Intel Lab dataset: `config/intel_nof_weights.ckpt` +- MIT CSAIL dataset: `config/mit_nof_weights.ckpt` + +## Run Experiments + +### Global Localization Experiments on IPBLab dataset +- Pre-training NOF on IPBLab dataset + ```shell + cd ~/ir-mcl + bash ./shells/pretraining/ipblab.sh + ``` +- Global localization experiments + ```shell + cd ir-mcl + python main.py --config_file ./config/global_localization/loc_config_{sequence_id}.yml + # for example: python main.py --config_file ./config/global_localization/loc_config_test1.yml + ``` + +- Pose-tracking experiments + ```shell + cd ir-mcl + python main.py --config_file ./config/pose_tracking/loc_config_{sequence_id}.yml + # for example: python main.py --config_file ./config/pose_tracking/loc_config_test1.yml + ``` + +### Observation Model Experiments +- Train/Test (replace "dataset" in "fr079", "intel", or "mit") + ```shell + cd ir-mcl + bash ./shells/pretraining/{dataset}.sh + # for example: bash ./shells/pretraining/intel.sh + ``` + +## Supplements for the Experimental Results +Due to the space limitation of the paper, we provide some experimental results as supplements here. + +### Memery cost +We provide an ablation study on the memory cost between the occupancy grid map (OGM), Hilbert map, and our neural occupancy field (NOF). + +| Maps type | Approximate memory | Loc. method | RMSE: location (cm) / yaw (degree) | +|----------------------|--------------------|--------------------------|----------------------------------------| +| OGM (5cm grid size) | 4.00MB | AMCL
NMCL
SRRG-Loc | 11.11/4.15
19.57/3.62
8.74/1.68 | +| OGM (10cm grid size) | 2.00MB | AMCL
NMCL
SRRG-Loc | 15.01/4.18
36.27/4.04
12.15/1.53 | +| Hilbert Map | 0.01MB | HMCL | 20.04/4.50 | +| NOF | 1.96NB | IR-MCL | **6.62**/**1.11** | + + +### Ablation study on fixed particle numbers +We also provide the experiment to study the performance of global localization under the same particle numbers for all methods. We fixed the number of particles to 100,000. In the below table, all baselines and IR-MCL always use 100,000 particles. IR-MCL is shown for reference. + +| Method | RMSE: location (cm) / yaw (degree) | +|-------------------------------------------------|----------------------------------------------------------------------| +| AMCL
NMCL
HMCL
SRRG-Loc
IR-MCL | 11.56/4.12
19.57/3.62
20.54/4.70
8.74/1.68
6.71/**1.11** | +| IR-MCL | **6.62**/**1.11** | + +## Citation + +If you use this library for any academic work, please cite our original [paper](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/kuang2023ral.pdf). + +```bibtex +@article{kuang2023ral, + author = {Kuang, Haofei and Chen, Xieyuanli and Guadagnino, Tiziano and Zimmerman, Nicky and Behley, Jens and Stachniss, Cyrill}, + title = {{IR-MCL: Implicit Representation-Based Online Global Localization}}, + journal = {IEEE Robotics and Automation Letters (RA-L)}, + doi = {10.1109/LRA.2023.3239318}, + year = {2023}, + codeurl = {https://github.com/PRBonn/ir-mcl}, +} +``` ## Acknowledgment This work has partially been funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement -No 101017008 (Harmony). +No 101017008 (Harmony). \ No newline at end of file diff --git a/config/fr079_nof_weights.ckpt b/config/fr079_nof_weights.ckpt new file mode 100644 index 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