https://github.com/prbonn/plants_temporal_matcher
This system can perform 3D point-to-point associations between plants' point clouds acquired in different session even in presence of highly repetitive structures and drastic changes.
https://github.com/prbonn/plants_temporal_matcher
Last synced: about 1 year ago
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This system can perform 3D point-to-point associations between plants' point clouds acquired in different session even in presence of highly repetitive structures and drastic changes.
- Host: GitHub
- URL: https://github.com/prbonn/plants_temporal_matcher
- Owner: PRBonn
- License: mit
- Created: 2023-08-30T14:00:00.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-11T13:41:13.000Z (almost 2 years ago)
- Last Synced: 2024-07-11T15:46:14.992Z (almost 2 years ago)
- Language: Python
- Size: 12.2 MB
- Stars: 25
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Estimating 4D Data Associations Towards Spatial-Temporal Mapping of Growing Plants for Agricultural Robots
This system can perform 3D point-to-point associations between plants' point clouds acquired in different session even in presence of highly repetitive structures and drastic changes.
**IMPORTANT**: if you are searching for a repository to compute temporal data association take a look to our [new repo](https://github.com/PRBonn/spatio-temporal-mapping), you will find a nice extension of this research.
## Installation
First, clone our repository
```bash
git clone git@github.com:PRBonn/plants_temporal_matcher.git && cd plants_temporal_matcher
```
Then, we recommend setting up a virtual environment of your choice and installing the provided requirements through:
```bash
pip install -r requirements.txt
```
## How to Use It
We propose __two__ scripts:
* __temporal_matcher.py__ -> it compute associations between the point cloud extracted from a single frame and a reference map (the script used to evaluate the system in our paper)
* __sparse_maps_matcher.py__ -> it takes two pre-computed maps and extract all the 3D point-to-point associations between them
In order to understand how to use the code it is important to keep in mind these __information__:
* The dataset is divided in __sessions__, each session is indicated by a number, ordered according to the time in which the recording was made
* We refer with the name __"reference"__ to the RGB-D sequence recorded first and with __"query"__ to the RGB-D sequence recorded after
* Each session is divided in __rows__, where each row is an actual different row in the glasshouse: of course, associations can be computed only between same rows
Type:
```bash
python temporal_matcher.py --help
```
or
```bash
python sparse_maps_matcher.py --help
```
to see how to run the scripts.
This is the output from the first script (temporal_matcher.py)
This is the output from the second script (sparse_maps_matcher.py)
This is an __example__ on how to call the script:
```bash
python temporal_matcher.py /path/to/the/dataset/ --ref-number 1 --query-number 2 --row-number 3 --render-matches --no-visualize-map
```
## Dataset
If you want to test this code on the dataset presented in the paper and reproducing the results, please send an email to [Luca Lobefaro](mailto:llobefar@uni-bonn.de?subject=[GitHub]%20Data%20Request).
## Publication
If you use our code in your academic work, please cite the corresponding [paper](https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/lobefaro2023iros.pdf):
```bibtex
@inproceedings{lobefaro2023iros,
author = {L. Lobefaro and M.V.R. Malladi and O. Vysotska and T. Guadagnino and C. Stachniss},
title = {{Estimating 4D Data Associations Towards Spatial-Temporal Mapping of Growing Plants for Agricultural Robots}},
booktitle = iros,
year = 2023,
codeurl = {https://github.com/PRBonn/plants_temporal_matcher}
}
```
## License
This project is free software made available under the MIT License. For details see the [LICENSE](https://github.com/PRBonn/plants_temporal_matcher/blob/main/LICENSE) file.