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sova
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SLAM on Voxel Alignment
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Download README
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README.md
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sova
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<img src="assets/logo.png"> SOVA is a SLAM on Voxel Alignment and an open-source Python library, designed for fast and adaptive comparison of different approaches to solving the voxel-based planar SLAM problem. Our main goal is to provide extendable, simple and efficient interfaces for testing various voxel SLAM hypotheses, which include different subdivision/segmenter/backend criteria. [](https://github.com/prime-slam/sova/actions/workflows/tests.yaml) [](https://opensource.org/licenses/Apache-2.0) - - - # Installation To use this library you need to: 1. Download and install Python 3.10 from the [official website](https://www.python.org/downloads/). 2. Install pip package: ```bash pip install sova ``` If you want to use `MROBBackend` robust optimisations, you have to install mrob library manually: 1. Download [wheel from source](https://drive.google.com/file/d/1EV6zuhYUTEwCgXr8i_kysAxgNvMivd2w/view?usp=sharing) 2. Install mrob from wheels ```bash python -m pip install mrob --no-index --find-links wheel/ --force-reinstall ``` Now you have everything you need to run your voxel-based pipeline. # Examples Examples of using the voxel-based pipeline are presented in the [`examples`](https://github.com/prime-slam/sova/tree/main/examples) directory with the all necessary instructions of how to run them. # Contributing To contribute to the project you must: 1. Get to know the project structure: ``` sova ├── backend ├── filter ├── pipeline ├── segmenter ├── subdivider ├── typing └── utils ├── dataset_reader ``` 2. Implement new subdivision/segmenter/backend approach which satisfy the relevant interface. 3. Create PullRequest to the repository. 4. Go through the review and wait for your code to appear in the main branch.
