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Carlos Sampedro 0001
Person information
- affiliation: Technical University of Madrid, Centre for Automation and Robotics, Spain
Other persons with the same name
- Carlos Sampedro 0002
— University of Granada, Department of Electronics, Spain
- Carlos Sampedro 0003 — Ecuela Politécnica Nacional (EPN), Quito, Ecuador
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2020 – today
- 2021
- [j10]Liang Lu
A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor. Remote. Sens. 13(9): 1796 (2021) - 2020
- [j9]Alejandro Rodriguez-Ramos
Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training. IEEE Access 8: 124451-124466 (2020)
2010 – 2019
- 2019
- [j8]Carlos Sampedro
Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings. IEEE Access 7: 101283-101308 (2019) - [j7]Alejandro Rodriguez-Ramos
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform. J. Intell. Robotic Syst. 93(1-2): 351-366 (2019) - [j6]Carlos Sampedro
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques. J. Intell. Robotic Syst. 95(2): 601-627 (2019) - 2018
- [j5]Carol Martínez
The Power Line Inspection Software (PoLIS): A versatile system for automating power line inspection. Eng. Appl. Artif. Intell. 71: 293-314 (2018) - [c5]Carlos Sampedro
Image-Based Visual Servoing Controller for Multirotor Aerial Robots Using Deep Reinforcement Learning. IROS 2018: 979-986 - [c4]Alejandro Rodriguez-Ramos
A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform. IROS 2018: 1010-1017 - [c3]Hriday Bavle
Stereo Visual Odometry and Semantics based Localization of Aerial Robots in Indoor Environments. IROS 2018: 1018-1023 - [c2]Carlos Sampedro
Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning. IROS 2018: 1024-1031 - 2017
- [j4]Martin Molina, Ramón A. Suárez Fernández
TML: a language to specify aerial robotic missions for the framework Aerostack. Int. J. Intell. Comput. Cybern. 10(4): 491-512 (2017) - [j3]José Luis Sánchez-López
A Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework. J. Intell. Robotic Syst. 88(2-4): 683-709 (2017) - [j2]Adrian Carrio
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles. J. Sensors 2017: 3296874:1-3296874:13 (2017) - [i1]Julian G. Zilly, Amit Boyarski, Micael Carvalho, Amir Atapour Abarghouei, Konstantinos Amplianitis, Aleksandr Krasnov, Massimiliano Mancini, Hernán Gonzalez, Riccardo Spezialetti, Carlos Sampedro Pérez, Hao Li:
Inspiring Computer Vision System Solutions. CoRR abs/1707.07210 (2017) - 2015
- [j1]Adrian Carrio, Carlos Sampedro
Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection. Sensors 15(11): 29569-29593 (2015) - 2014
- [c1]Carlos Sampedro
A supervised approach to electric tower detection and classification for power line inspection. IJCNN 2014: 1970-1977
Coauthor Index
aka: Pascual Campoy
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