{"id":18792429,"url":"https://github.com/prbonn/plants_temporal_matcher","last_synced_at":"2025-04-13T14:31:12.193Z","repository":{"id":191646892,"uuid":"685094233","full_name":"PRBonn/plants_temporal_matcher","owner":"PRBonn","description":"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 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align=\"center\"\u003e\n  \u003ch1\u003eEstimating 4D Data Associations Towards Spatial-Temporal Mapping of Growing Plants for Agricultural Robots\u003c/h1\u003e\n  \u003ca href=\"https://github.com/PRBonn/plants_temporal_matcher#how-to-use-it\"\u003e\u003cimg src=\"https://img.shields.io/badge/python-3670A0?style=flat-square\u0026logo=python\u0026logoColor=ffdd54\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://github.com/PRBonn/plants_temporal_matcher#installation\"\u003e\u003cimg src=\"https://img.shields.io/badge/Linux-FCC624?logo=linux\u0026logoColor=black\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://www.ipb.uni-bonn.de/wp-content/papercite-data/pdf/lobefaro2023iros.pdf\"\u003e\u003cimg src=\"https://img.shields.io/badge/Paper-pdf-\u003cCOLOR\u003e.svg?style=flat-square\" /\u003e\u003c/a\u003e\n    \u003ca href=\"https://github.com/PRBonn/plants_temporal_matcher/blob/main/LICENSE\"\u003e\u003cimg src=\"https://img.shields.io/badge/License-MIT-blue.svg?style=flat-square\" /\u003e\u003c/a\u003e\n\n\u003cp\u003e\n  \u003cimg src=\"https://github.com/PRBonn/plants_temporal_matcher/blob/main/images/first_image.png\" width=\"700\"/\u003e\n\u003c/p\u003e\n\n\u003cp\u003e\n  \u003ci\u003eThis 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.\u003c/i\u003e\n\u003c/p\u003e\n\n\u003c/div\u003e\n\n\n**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.\n\n## Installation\nFirst, clone our repository\n```bash\ngit clone git@github.com:PRBonn/plants_temporal_matcher.git \u0026\u0026 cd plants_temporal_matcher\n```\n\nThen, we recommend setting up a virtual environment of your choice and installing the provided requirements through:\n```bash\npip install -r requirements.txt\n```\n\n\n\n## How to Use It\nWe propose __two__ scripts:\n* __temporal_matcher.py__ -\u003e 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)\n* __sparse_maps_matcher.py__ -\u003e it takes two pre-computed maps and extract all the 3D point-to-point associations between them\n\nIn order to understand how to use the code it is important to keep in mind these __information__:\n* The dataset is divided in __sessions__, each session is indicated by a number, ordered according to the time in which the recording was made\n* We refer with the name __\"reference\"__ to the RGB-D sequence recorded first and with __\"query\"__ to the RGB-D sequence recorded after\n* 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\n\n\nType:\n```bash\npython temporal_matcher.py --help\n```\nor\n```bash\npython sparse_maps_matcher.py --help\n```\nto see how to run the scripts.\n\u003cdetails\u003e\n\u003csummary\u003eThis is the output from the first script (\u003cstrong\u003etemporal_matcher.py\u003c/strong\u003e) \u003c/summary\u003e\n\n![temporal matcher help](https://github.com/PRBonn/plants_temporal_matcher/blob/main/images/temporal_matcher_help.png)\n\n\u003c/details\u003e\n\u003cdetails\u003e\n\u003csummary\u003eThis is the output from the second script (\u003cstrong\u003esparse_maps_matcher.py\u003c/strong\u003e) \u003c/summary\u003e\n\n![sparse maps matcher help](https://github.com/PRBonn/plants_temporal_matcher/blob/main/images/sparse_temporal_matcher_help.png)\n\n\u003c/details\u003e\n\nThis is an __example__ on how to call the script:\n```bash\npython temporal_matcher.py /path/to/the/dataset/ --ref-number 1 --query-number 2 --row-number 3 --render-matches --no-visualize-map \n```\n\n\n\n## Dataset\nIf 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).\n\n\n\n## Publication\nIf 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):\n\n```bibtex\n@inproceedings{lobefaro2023iros,\n  author = {L. Lobefaro and M.V.R. Malladi and O. Vysotska and T. Guadagnino and C. Stachniss},\n  title = {{Estimating 4D Data Associations Towards Spatial-Temporal Mapping of Growing Plants for Agricultural Robots}},\n  booktitle = iros,\n  year = 2023,\n  codeurl = {https://github.com/PRBonn/plants_temporal_matcher}\n}\n```\n\n\n\n## License\nThis 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.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprbonn%2Fplants_temporal_matcher","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprbonn%2Fplants_temporal_matcher","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprbonn%2Fplants_temporal_matcher/lists"}