| Name | mad-icp JSON |
| Version |
0.0.8
JSON |
| download |
| home_page | None |
| Summary | It Is All About Matching Data -- Robust and Informed LiDAR Odometry |
| upload_time | 2024-11-21 18:40:01 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
lidar
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<div align="center">
<a href="https://github.com/rvp-group/mad-icp/actions/workflows/pythonbuild.yml"><img src="https://github.com/rvp-group/mad-icp/actions/workflows/pythonbuild.yml/badge.svg"/></a>
<a href="https://github.com/rvp-group/mad-icp"><img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/rvp-group/mad-icp"></a>
<a href="https://pypi.org/project/mad-icp/"><img alt="PyPI - Version" src="https://img.shields.io/pypi/v/mad-icp"></a>
<a href="https://pypi.org/project/mad-icp/"><img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/mad-icp"></a>
<h1>MAD-ICP</h1>
<h3>It Is All About Matching Data -- Robust and Informed LiDAR Odometry</h3>
<h3>Accepted <a href="https://ieeexplore.ieee.org/document/10669999">RA-L</a> 2024</h3>
<h3>
<a href="https://github.com/rvp-group/mad-icp/blob/main/paper_with_supplementary.pdf">Preprint</a>
</h3>
<div align="center">
<a href="https://github.com/rvp-group/mad-icp"><img src="https://github.com/rvp-group/mad-icp/blob/main/mad-icp.gif?raw=true"/></a>
</div>
<br />
</div>
# Install using `pip`
You can download/install MAD-ICP using `pip`
```bash
pip install mad-icp
```
# Usage
We provide a Python launcher for Rosbag1, Rosbag2, and KITTI binary formats. The dataset configuration is important for the sensor characteristics and extrinsic information (typically, ground truths are not expressed in the LiDAR frame). In `configurations/datasets/dataset_configurations.py` we provide configurations for many datasets.
The internal parameters (used by default) are stored in `configurations/mad_params.py`. All the experiments have been run with this same set.
You can specify a new set in `configurations/mad_params.py` and use it with the option `--mad-icp-params`.
Both the options `--dataset-config` and `--mad-icp-params` also accept `.cfg` files like those in `configurations`.
To run the pipeline, choose the appropriate dataset configuration (`kitti` for this example) and type:
```bash
mad_icp --data-path /input_dir/ \
--estimate-path /output_dir/ \
--dataset-config kitti
```
Our runner directly saves the odometry estimate file in KITTI format (homogenous matrix row-major 12 scalars); soon, we will provide more available formats like TUM.
Our pipeline is `anytime realtime`! You can play with parameters `num_keyframes` and `num_cores` and, if you have enough _computation capacity_, we suggest increasing these (we run demo/experiments with `num_keyframes=16` and `num_cores=16`).
## Data associtation and registration tools
If you want to use our MAD-tree to perform nearest neighbor or use MAD-ICP to perform registration between two point clouds, <b>[here few easy examples](https://github.com/rvp-group/mad-icp/tree/main/mad_icp/apps/utils/tools/README.md)</b>.
____________________________________________________________________
# Building from source
Building is tested by our CI/CD pipeline for Ubuntu 20.04 and Ubuntu 22.04 (using g++).
The following external dependencies are required.
| Dependency | Version(s) known to work |
| ------------ | ------------------------ |
| [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) | 3.3 |
| [OpenMP](https://www.openmp.org/) | |
| [pybind11](https://pybind11.readthedocs.io/en/stable/) | |
| [yaml](https://github.com/jbeder/yaml-cpp) (optional for C++ apps) | |
If your system lacks any dependency (except for `OpenMP`) we download local copies using `FetchContent`.
If you want to build and install the package, assuming you're inside the repository, you can use `pip` as follows:
```bash
pip install .
```
Moreover, you can build the C++ library (along with the pybinds) by typing:
```bash
mkdir build && cd build && cmake ../mad_icp && make -j
```
## Building and Running C++ Apps \[Optional\]
If you want to avoid Python, we provide the `bin_runner` C++ executable (located in `mad_icp/apps/cpp_runners/bin_runner.cpp`) that accepts binary cloud format (KITTI, Mulran, etc.).
You can build the executable using
```bash
mkdir build && cd build && cmake -DCOMPILE_CPP_APPS=ON ../mad_icp && make -j
```
And run
```bash
cd build/apps/cpp_runners
./bin_runner -data_path /path_to_bag_folder/ \
-estimate_path /path_to_estimate_folder/ \
-dataset_config ../../../mad_icp/configurations/datasets/kitti.cfg \
-mad_icp_config ../../../mad_icp/configurations/default.cfg
```
>[!IMPORTANT]
>If running on the KITTI dataset, enable the flag `-kitti` for KITTI scan correction (not documented anywhere). We do not (currently) provide a viewer for this executable.
# What is missing?
- ROS/ROS2 optional dependencies
# Cite us
If you use any of this code, please cite our <a href="https://ieeexplore.ieee.org/document/10669999">paper</a>:
```
@article{ferrari2024mad,
title={MAD-ICP: It Is All About Matching Data--Robust and Informed LiDAR Odometry},
author={Ferrari, Simone and Di Giammarino, Luca and Brizi, Leonardo and Grisetti, Giorgio},
journal={IEEE Robotics and Automation Letters},
year={2024},
doi={10.1109/LRA.2024.3456509}
}
```
Raw data
{
"_id": null,
"home_page": null,
"name": "mad-icp",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "LiDAR",
"author": null,
"author_email": "Simone Ferrari <s.ferrari@diag.uniroma1.it>, Luca Di Giammarino <digiammarino@diag.uniroma1.it>, Leonardo Brizi <brizi@diag.uniroma1.it>, Emanuele Giacomini <giacomini@diag.uniroma1.it>",
"download_url": "https://files.pythonhosted.org/packages/9a/2c/c22c32ea3de200251e6f18ea010b94fbfd89cf86ff04492da0a91a7d6e67/mad_icp-0.0.8.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n <a href=\"https://github.com/rvp-group/mad-icp/actions/workflows/pythonbuild.yml\"><img src=\"https://github.com/rvp-group/mad-icp/actions/workflows/pythonbuild.yml/badge.svg\"/></a>\n <a href=\"https://github.com/rvp-group/mad-icp\"><img alt=\"GitHub Repo stars\" src=\"https://img.shields.io/github/stars/rvp-group/mad-icp\"></a>\n <a href=\"https://pypi.org/project/mad-icp/\"><img alt=\"PyPI - Version\" src=\"https://img.shields.io/pypi/v/mad-icp\"></a>\n <a href=\"https://pypi.org/project/mad-icp/\"><img alt=\"PyPI - Downloads\" src=\"https://img.shields.io/pypi/dm/mad-icp\"></a>\n <h1>MAD-ICP</h1>\n <h3>It Is All About Matching Data -- Robust and Informed LiDAR Odometry</h3>\n <h3>Accepted <a href=\"https://ieeexplore.ieee.org/document/10669999\">RA-L</a> 2024</h3>\n <h3>\n <a href=\"https://github.com/rvp-group/mad-icp/blob/main/paper_with_supplementary.pdf\">Preprint</a>\n </h3>\n <div align=\"center\">\n <a href=\"https://github.com/rvp-group/mad-icp\"><img src=\"https://github.com/rvp-group/mad-icp/blob/main/mad-icp.gif?raw=true\"/></a> \n </div>\n <br /> \n</div>\n\n# Install using `pip`\nYou can download/install MAD-ICP using `pip`\n```bash\npip install mad-icp\n```\n\n# Usage\n\nWe provide a Python launcher for Rosbag1, Rosbag2, and KITTI binary formats. The dataset configuration is important for the sensor characteristics and extrinsic information (typically, ground truths are not expressed in the LiDAR frame). In `configurations/datasets/dataset_configurations.py` we provide configurations for many datasets.\n\nThe internal parameters (used by default) are stored in `configurations/mad_params.py`. All the experiments have been run with this same set.\nYou can specify a new set in `configurations/mad_params.py` and use it with the option `--mad-icp-params`.\n\nBoth the options `--dataset-config` and `--mad-icp-params` also accept `.cfg` files like those in `configurations`.\n\nTo run the pipeline, choose the appropriate dataset configuration (`kitti` for this example) and type:\n```bash\nmad_icp --data-path /input_dir/ \\\n --estimate-path /output_dir/ \\\n --dataset-config kitti\n```\nOur runner directly saves the odometry estimate file in KITTI format (homogenous matrix row-major 12 scalars); soon, we will provide more available formats like TUM.\n\nOur pipeline is `anytime realtime`! You can play with parameters `num_keyframes` and `num_cores` and, if you have enough _computation capacity_, we suggest increasing these (we run demo/experiments with `num_keyframes=16` and `num_cores=16`).\n\n## Data associtation and registration tools\nIf you want to use our MAD-tree to perform nearest neighbor or use MAD-ICP to perform registration between two point clouds, <b>[here few easy examples](https://github.com/rvp-group/mad-icp/tree/main/mad_icp/apps/utils/tools/README.md)</b>.\n\n\n____________________________________________________________________\n\n# Building from source\n\nBuilding is tested by our CI/CD pipeline for Ubuntu 20.04 and Ubuntu 22.04 (using g++).\n\nThe following external dependencies are required.\n| Dependency | Version(s) known to work |\n| ------------ | ------------------------ |\n| [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) | 3.3 |\n| [OpenMP](https://www.openmp.org/) | |\n| [pybind11](https://pybind11.readthedocs.io/en/stable/) | |\n| [yaml](https://github.com/jbeder/yaml-cpp) (optional for C++ apps) | |\n\nIf your system lacks any dependency (except for `OpenMP`) we download local copies using `FetchContent`.\nIf you want to build and install the package, assuming you're inside the repository, you can use `pip` as follows:\n```bash\npip install .\n```\nMoreover, you can build the C++ library (along with the pybinds) by typing:\n```bash\nmkdir build && cd build && cmake ../mad_icp && make -j\n```\n\n## Building and Running C++ Apps \\[Optional\\]\nIf you want to avoid Python, we provide the `bin_runner` C++ executable (located in `mad_icp/apps/cpp_runners/bin_runner.cpp`) that accepts binary cloud format (KITTI, Mulran, etc.).\nYou can build the executable using\n```bash\nmkdir build && cd build && cmake -DCOMPILE_CPP_APPS=ON ../mad_icp && make -j\n```\nAnd run\n```bash\ncd build/apps/cpp_runners\n./bin_runner -data_path /path_to_bag_folder/ \\\n -estimate_path /path_to_estimate_folder/ \\\n -dataset_config ../../../mad_icp/configurations/datasets/kitti.cfg \\\n -mad_icp_config ../../../mad_icp/configurations/default.cfg \n```\n>[!IMPORTANT]\n >If running on the KITTI dataset, enable the flag `-kitti` for KITTI scan correction (not documented anywhere). We do not (currently) provide a viewer for this executable. \n\n# What is missing?\n- ROS/ROS2 optional dependencies\n\n# Cite us\nIf you use any of this code, please cite our <a href=\"https://ieeexplore.ieee.org/document/10669999\">paper</a>:\n\n```\n@article{ferrari2024mad,\n title={MAD-ICP: It Is All About Matching Data--Robust and Informed LiDAR Odometry},\n author={Ferrari, Simone and Di Giammarino, Luca and Brizi, Leonardo and Grisetti, Giorgio},\n journal={IEEE Robotics and Automation Letters},\n year={2024},\n doi={10.1109/LRA.2024.3456509}\n}\n```\n",
"bugtrack_url": null,
"license": null,
"summary": "It Is All About Matching Data -- Robust and Informed LiDAR Odometry",
"version": "0.0.8",
"project_urls": {
"Homepage": "https://github.com/rvp-group/mad-icp"
},
"split_keywords": [
"lidar"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "9a2cc22c32ea3de200251e6f18ea010b94fbfd89cf86ff04492da0a91a7d6e67",
"md5": "501288f625eb7a9445e382d06b35013a",
"sha256": "81ae68d3e6c465c0a325fa31c512d60a56ddaa851920b8b6168701879eab47be"
},
"downloads": -1,
"filename": "mad_icp-0.0.8.tar.gz",
"has_sig": false,
"md5_digest": "501288f625eb7a9445e382d06b35013a",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 21301376,
"upload_time": "2024-11-21T18:40:01",
"upload_time_iso_8601": "2024-11-21T18:40:01.106428Z",
"url": "https://files.pythonhosted.org/packages/9a/2c/c22c32ea3de200251e6f18ea010b94fbfd89cf86ff04492da0a91a7d6e67/mad_icp-0.0.8.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-21 18:40:01",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "rvp-group",
"github_project": "mad-icp",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "mad-icp"
}
