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Tool for creating optimally sized databases (containing the minimum number of frames covering the scene) for place recognition task from RGBD and LiDAR data
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prime-slam/place-recognition-db
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PlaceRecognitionDB is a tool for creating optimally sized databases(containing the minimum number of frames covering the scene) for place recognition task from RGBD and LiDAR data.
The tool supports several basic methods for creating databases,as well as the DominatingSet method for creating optimal databases.A detailed description of all methods for creating databases can be found in ourpaper.
The tool also contains a global localization pipeline withCosPlaceandNetVLAD.The models of these neural networks can be fine-tuned for a particular previously created database.The results of global localization can also be improved withSuperGlue.More details about the available configurations for global localization and the resultsare available in thepaper.
We have also developed a set of metrics that can be used to evaluatethe quality of created databases and the accuracy of VPR systems.
For more, please visit thePlaceRecognitionDB documentation.You can also find full information about our research on thewebsite.
You can find detailed information about the data format used in the toolhere.
Please checkexample.ipynb
with a small example on creating a database.
This project is licensed under the Apache License —see theLICENSE file for details.
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Tool for creating optimally sized databases (containing the minimum number of frames covering the scene) for place recognition task from RGBD and LiDAR data