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In the last decade, autonomous navigation for roboticshas been leveraged bydeep learning and other approachesbased on machine learning. These approacheshave demon-strated significant advantages in robotics performance. Butthey havethe disadvantage that they require a lot of data toinfer knowledge. In thispaper, we present an algorithm forbuilding 2D maps with attributes that makethem useful fortraining and testing machine-learning-based approaches.The mapsare based on dungeons environments where sev-eral random rooms are built andthen those rooms are con-nected. In addition, we provide a dataset with 10,000mapsproduced by the proposed algorithm and a description withextensiveinformation for algorithm evaluation. Such infor-mation includes validation ofpath existence, the best path,distances, among other attributes. We believethat thesemaps and their related information can be very useful forroboticsenthusiasts and researchers who want to test deeplearning approaches. Thedataset is available athttps://github.com/gbriel21/map2D_dataSet.git

2D Grid Map Generation for Deep-Learning-based Navigation Approaches