Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Mariano Morán"'
Autor:
Jorge L. Martínez, Jesús Morales, Manuel Sánchez, Mariano Morán, Antonio J. Reina, J. Jesús Fernández-Lozano
Publikováno v:
Sensors, Vol 20, Iss 22, p 6423 (2020)
Reactivity is a key component for autonomous vehicles navigating on natural terrains in order to safely avoid unknown obstacles. To this end, it is necessary to continuously assess traversability by processing on-board sensor data. This paper describ
Externí odkaz:
https://doaj.org/article/929649da847a4114ad8d5e88b7f11aec
Publikováno v:
Applied Sciences, Vol 10, Iss 3, p 1140 (2020)
Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata o
Externí odkaz:
https://doaj.org/article/931f23efe9874f248146a74830dec770
Publikováno v:
Applied Sciences, Vol 8, Iss 3, p 397 (2018)
The paper describes the case study of the mobile robot Andabata navigating on natural terrain at low speeds with fuzzy elevation maps (FEMs). To this end, leveled three-dimensional (3D) point clouds of the surroundings are obtained by synchronizing r
Externí odkaz:
https://doaj.org/article/b79b715eac1c4920a3c3fc4869a1f85a
Autor:
Jesus Morales, Manuel Sánchez, J. Fernandez-Lozano, Mariano Morán, Jorge L. Martínez, Antonio J. Reina
Publikováno v:
Sensors, Vol 20, Iss 6423, p 6423 (2020)
Sensors (Basel, Switzerland)
Sensors (Basel, Switzerland)
Reactivity is a key component for autonomous vehicles navigating on natural terrains in order to safely avoid unknown obstacles. To this end, it is necessary to continuously assess traversability by processing on-board sensor data. This paper describ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::63d7bb7dabd00c3659db2000815e13cd
https://www.mdpi.com/1424-8220/20/22/6423
https://www.mdpi.com/1424-8220/20/22/6423
Publikováno v:
ICM
Progress in applying supervised learning for natural scene classification is impeded by the lack of appropriate datasets for training. This paper describes the automatic generation of synthetic three-dimensional (3D) scans of natural environments wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::697c671d37af303e66336076d21a828f
https://doi.org/10.1109/icmech.2019.8722866
https://doi.org/10.1109/icmech.2019.8722866
Publikováno v:
Applied Sciences, Vol 10, Iss 3, p 1140 (2020)
Applied Sciences
Volume 10
Issue 3
Applied Sciences
Volume 10
Issue 3
Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da3c8f6aea019156e4f35e874dad7d16
https://doi.org/10.3390/app10031140
https://doi.org/10.3390/app10031140
Autor:
Jorge L. Martínez, Mariano Morán, María A. Martínez, Jesus Morales, Anthony Mandow, Antonio Pomares
Publikováno v:
MED
Ground extraction from three-dimensional (3D) range data is a relevant problem for outdoor navigation of unmanned ground vehicles. Even if this problem has received attention with specific heuristics and segmentation approaches, identification of gro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5e0324268f0fab88ada3ac982c1fcbd
https://doi.org/10.1109/med.2018.8442569
https://doi.org/10.1109/med.2018.8442569
Publikováno v:
Applied Sciences; Volume 8; Issue 3; Pages: 397
Applied Sciences, Vol 8, Iss 3, p 397 (2018)
Applied Sciences, Vol 8, Iss 3, p 397 (2018)
The paper describes the case study of the mobile robot Andabata navigating on natural terrain at low speeds with fuzzy elevation maps (FEMs). To this end, leveled three-dimensional (3D) point clouds of the surroundings are obtained by synchronizing r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f9302f9e71282a2a57d69eb24752649