https://github.com/tub-rip/cmax_slam
Official implementation of IEEE T-RO 2024 paper: "CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization"
https://github.com/tub-rip/cmax_slam
asynchronous-sensor contrast-maximization event-camera hdr high-dynamic-range ieee-tro rotational-motion simultaneous-localization-and-mapping slam
Last synced: 8 months ago
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Official implementation of IEEE T-RO 2024 paper: "CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization"
- Host: GitHub
- URL: https://github.com/tub-rip/cmax_slam
- Owner: tub-rip
- License: mit
- Created: 2024-03-14T17:01:59.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-12T22:13:06.000Z (12 months ago)
- Last Synced: 2025-03-12T23:22:29.279Z (12 months ago)
- Topics: asynchronous-sensor, contrast-maximization, event-camera, hdr, high-dynamic-range, ieee-tro, rotational-motion, simultaneous-localization-and-mapping, slam
- Language: C++
- Homepage:
- Size: 4.43 MB
- Stars: 51
- Watchers: 2
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- event-vision-index - CMax-SLAM (TRO 2024) - rip/cmax_slam?style=social"/> (Visual Odometry and SLAM / Rotational motion (3-DOF))
README
# CMax-SLAM
Official repository for [**CMax-SLAM: Event-based Rotational-Motion Bundle Adjustment and SLAM System using Contrast Maximization**](https://arxiv.org/pdf/2403.08119), **IEEE T-RO 2024**, by [Shuang Guo](https://shuang1997.github.io/) and [Guillermo Gallego](http://www.guillermogallego.es).
[Paper](https://doi.org/10.1109/TRO.2024.3378443) | [PDF](https://arxiv.org/pdf/2403.08119) | [Poster](https://drive.google.com/file/d/1Y1xkWi1Xxfr7SlKqGOsKtRBfPG9cMthk/view?usp=sharing) | [Video](https://youtu.be/17VWTuSkgPs) | [Dataset](https://github.com/tub-rip/ECRot)
[](https://youtu.be/17VWTuSkgPs)
## Citation
If you use this work in your research, please consider citing:
```bibtex
@Article{Guo24tro,
author = {Shuang Guo and Guillermo Gallego},
title = {{CMax}-{SLAM}: Event-based Rotational-Motion Bundle Adjustment
and {SLAM} System using Contrast Maximization},
journal = {{IEEE} Transactions on Robotics},
year = 2024,
volume = {40},
number = {},
pages = {2442--2461},
doi = {10.1109/TRO.2024.3378443}
}
@InProceedings{Gallego18cvpr,
author = {Guillermo Gallego and Henri Rebecq and Davide Scaramuzza},
title = {A Unifying Contrast Maximization Framework for Event Cameras,
with Applications to Motion, Depth, and Optical Flow
Estimation},
booktitle = {{IEEE} Conf. Comput. Vis. Pattern Recog. (CVPR)},
year = 2018,
pages = {3867--3876},
doi = {10.1109/CVPR.2018.00407}
}
```
-------
## Setup
### High-level Input-Output
**Input**:
- Events.
- Camera calibration.
**Output**:
- Rotational motion of the event camera.
- Local (front-end) and global (back-end) image of warped events (IWEs).
## Usage
- [Installation](docs/installation.md)
- [Test on Datasets](docs/test_datasets.md)
- [Live demo with a DAVIS camera](docs/live_demo.md)
- [Parameter Guide](docs/patermeters.md)
## [Event Camera Rotation Dataset (ECRot)](https://github.com/tub-rip/ECRot)
We also release the dataset that we made, see and download it from [here](https://github.com/tub-rip/ECRot).
The instructions of running CMax-SLAM on the ECRot datasets can be found [here](https://github.com/tub-rip/cmax_slam/blob/main/docs/test_datasets.md).
## Acknowledgements
This code leverages the following repository for computing the derivative of Lie Group B-splines:
- [Basalt Headers](https://gitlab.com/VladyslavUsenko/basalt-headers)
## Additional Resources
* [Research page (TU Berlin RIP lab)](https://sites.google.com/view/guillermogallego/research/event-based-vision)
* [Course at TU Berlin](https://sites.google.com/view/guillermogallego/teaching/event-based-robot-vision)
* [EPBA: Event-based Photometric Bundle Adjustment](https://github.com/tub-rip/epba)
* [EMBA: Event-based Mosaicing Bundle Adjustment (ECCV 2024)](https://github.com/tub-rip/emba)
* [Secrets of Event-Based Optical Flow (TPAMI 2024)](https://github.com/tub-rip/event_based_optical_flow)
* [ES-PTAM: Event-based Stereo Parallel Tracking and Mapping](https://github.com/tub-rip/ES-PTAM)
* [Survey paper](http://rpg.ifi.uzh.ch/docs/EventVisionSurvey.pdf)
* [List of Resources](https://github.com/uzh-rpg/event-based_vision_resources)