ika-rwth-aachen/EviLOG
TensorFlow training pipeline and dataset for prediction of evidential occupancy grid maps from lidar point clouds.
This project helps automotive engineers and researchers develop better self-driving car systems by providing a dataset and a method for converting raw lidar sensor data into detailed occupancy grid maps. These maps show the environment around a vehicle, including how certain it is about obstacles and open spaces. It's used by those building or evaluating autonomous vehicle perception systems.
No commits in the last 6 months.
Use this if you need to train a system to accurately predict evidential occupancy grid maps from lidar point clouds for autonomous vehicle navigation, especially when uncertainty quantification is critical.
Not ideal if you're looking for a pre-built, ready-to-deploy occupancy grid mapping solution for commercial use without any development or training.
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Language
Python
License
MIT
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Last pushed
Aug 07, 2024
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