TY - GEN
T1 - 2D Grid Map Generation for Deep-Learning-based Navigation Approaches
AU - Flores-Aquino, Gabriel O.
AU - Ortega, Jheison Duvier Diaz
AU - Arvizu, Ricardo Yahir Almazan
AU - Gutierrez-Frias, O. Octavio
AU - Munoz, Raul L.
AU - Vasquez-Gomez, J. Irving
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In the last decade, autonomous navigation for robotics has been leveraged by deep learning and other approaches based on machine learning. These approaches have demonstrated significant advantages in robotics performance. But they have the disadvantage that they require a lot of data to infer knowledge. In this paper, we present an algorithm for building 2D maps with attributes that make them useful for training and testing machine-learning-based approaches. The maps are based on dungeons environments where several random rooms are built and then those rooms are connected. In addition, we provide a dataset with 10,000 maps produced by the proposed algorithm and a description with extensive information for algorithm evaluation. Such information includes validation of path existence, the best path, distances, among other attributes. We believe that these maps and their related information can be useful for robotics enthusiasts and researchers who want to test deep learning approaches. The dataset is available at https://github.com/gbriel21/map2D dataSet.git.
AB - In the last decade, autonomous navigation for robotics has been leveraged by deep learning and other approaches based on machine learning. These approaches have demonstrated significant advantages in robotics performance. But they have the disadvantage that they require a lot of data to infer knowledge. In this paper, we present an algorithm for building 2D maps with attributes that make them useful for training and testing machine-learning-based approaches. The maps are based on dungeons environments where several random rooms are built and then those rooms are connected. In addition, we provide a dataset with 10,000 maps produced by the proposed algorithm and a description with extensive information for algorithm evaluation. Such information includes validation of path existence, the best path, distances, among other attributes. We believe that these maps and their related information can be useful for robotics enthusiasts and researchers who want to test deep learning approaches. The dataset is available at https://github.com/gbriel21/map2D dataSet.git.
KW - dataset
KW - deep-learning
KW - map-based navigation
KW - occupancy grid maps
KW - robotics
UR - http://www.scopus.com/inward/record.url?scp=85140931916&partnerID=8YFLogxK
U2 - 10.1109/ICMEAE55138.2021.00018
DO - 10.1109/ICMEAE55138.2021.00018
M3 - Contribución a la conferencia
AN - SCOPUS:85140931916
T3 - Proceedings - 2021 International Conference on Mechatronics, Electronics and Automotive Engineering, ICMEAE 2021
SP - 66
EP - 70
BT - Proceedings - 2021 International Conference on Mechatronics, Electronics and Automotive Engineering, ICMEAE 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 International Conference on Mechatronics, Electronics and Automotive Engineering, ICMEAE 2021
Y2 - 22 November 2021 through 26 November 2021
ER -