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https://github.com/gradslam/gradslam
gradslam is an open source differentiable dense SLAM library for PyTorch
https://github.com/gradslam/gradslam
3d-reconstruction deep-learning machine-learning robotics slam
Last synced: about 1 month ago
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gradslam is an open source differentiable dense SLAM library for PyTorch
- Host: GitHub
- URL: https://github.com/gradslam/gradslam
- Owner: gradslam
- License: mit
- Created: 2020-11-06T03:09:31.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-09-02T16:35:54.000Z (10 months ago)
- Last Synced: 2024-02-04T15:19:35.955Z (4 months ago)
- Topics: 3d-reconstruction, deep-learning, machine-learning, robotics, slam
- Language: Python
- Homepage: https://gradslam.github.io/
- Size: 64.7 MB
- Stars: 1,251
- Watchers: 46
- Forks: 151
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Lists
- awesome-robotic-tooling - gradslam - An open source differentiable dense SLAM library for PyTorch. (Simultaneous Localization and Mapping / Visual)
- awesome-robotic-tooling - gradslam - An open source differentiable dense SLAM library for PyTorch. (Simultaneous Localization and Mapping / Visual)
- awesome-mobile-robotics - GradSLAM
README
--------------------------------------------------------------------------------
[](https://opensource.org/licenses/MIT) [](https://circleci.com/gh/gradslam/gradslam/tree/master) [](https://gradslam.readthedocs.io/en/latest/?badge=latest) [](https://badge.fury.io/py/gradslam)
- [Overview](#overview)
- [Installation](#installation)
- [Tutorials](https://gradslam.readthedocs.io/en/latest/tutorials.html)
- [Online Documentation](https://gradslam.readthedocs.io/en/latest/)
- [Contributing to gradslam](CONTRIBUTING.md)
## Overview
gradslam is a fully differentiable dense SLAM framework. It provides a repository of differentiable building blocks for a dense SLAM system, such as differentiable nonlinear least squares solvers, differentiable ICP (iterative closest point) techniques, differentiable raycasting modules, and differentiable mapping/fusion blocks. One can use these blocks to construct SLAM systems that allow gradients to flow all the way from the outputs of the system (map, trajectory) to the inputs (raw color/depth images, parameters, calibration, etc.).
```python
rgbdimages = RGBDImages(colors, depths, intrinsics)
slam = PointFusion()
pointclouds, recovered_poses = slam(rgbdimages)
pointclouds.plotly(0).show()
```
## Installation
### Requirements
- `pytorch>=1.6.0` (for other pytorch versions see [here](#install-from-local-clone-recommended))
### Using pip (Experimental)
`pip install gradslam`
### Install from GitHub
`pip install 'git+https://github.com/gradslam/gradslam.git'`
### Install from local clone (Recommended)
```
git clone https://github.com/krrish94/chamferdist.git
cd chamferdist
pip install .
cd ..
git clone https://github.com/gradslam/gradslam.git
cd gradslam
pip install -e .[dev]
```
### Verifying the installation
To verify if `gradslam` has successfully been built, fire up the python interpreter, and import!
```py
import gradslam as gs
print(gs.__version__)
```
You should see the version number displayed.
## Citing gradslam
If you find `gradslam` useful in your work, and are writing up a report/paper about us, we'd appreciate if you cited us. Please use the following bibtex entry.
```
@inproceedings{gradslam,
title={gradSLAM: Dense SLAM meets automatic differentiation},
author={{Krishna Murthy}, Jatavallabhula and Saryazdi, Soroush and Iyer, Ganesh and Paull, Liam},
booktitle={arXiv},
year={2020},
}
```
## Contributors
* Soroush Saryazdi
* Krishna Murthy Jatavallabhula
* Ganesh Iyer