Carlos Sampedro

Orcid: 0000-0003-2414-2284

Affiliations:
  • Technical University of Madrid, Centre for Automation and Robotics, Spain


According to our database1, Carlos Sampedro authored at least 16 papers between 2014 and 2021.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

Online presence:

On csauthors.net:

Bibliography

2021
A Robust and Fast Collision-Avoidance Approach for Micro Aerial Vehicles Using a Depth Sensor.
Remote. Sens., 2021

2020
Adaptive Inattentional Framework for Video Object Detection With Reward-Conditional Training.
IEEE Access, 2020

2019
A Fully-Autonomous Aerial Robot for Search and Rescue Applications in Indoor Environments using Learning-Based Techniques.
J. Intell. Robotic Syst., 2019

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Moving Platform.
J. Intell. Robotic Syst., 2019

Deep Learning-Based System for Automatic Recognition and Diagnosis of Electrical Insulator Strings.
IEEE Access, 2019

2018
The Power Line Inspection Software (PoLIS): A versatile system for automating power line inspection.
Eng. Appl. Artif. Intell., 2018

Image-Based Visual Servoing Controller for Multirotor Aerial Robots Using Deep Reinforcement Learning.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Laser-Based Reactive Navigation for Multirotor Aerial Robots using Deep Reinforcement Learning.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

A Deep Reinforcement Learning Technique for Vision-Based Autonomous Multirotor Landing on a Moving Platform.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

Stereo Visual Odometry and Semantics based Localization of Aerial Robots in Indoor Environments.
Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2018

2017
A Review of Deep Learning Methods and Applications for Unmanned Aerial Vehicles.
J. Sensors, 2017

A Multi-Layered Component-Based Approach for the Development of Aerial Robotic Systems: The Aerostack Framework.
J. Intell. Robotic Syst., 2017

TML: a language to specify aerial robotic missions for the framework Aerostack.
Int. J. Intell. Comput. Cybern., 2017

Inspiring Computer Vision System Solutions.
CoRR, 2017

2015
Automated Low-Cost Smartphone-Based Lateral Flow Saliva Test Reader for Drugs-of-Abuse Detection.
Sensors, 2015

2014
A supervised approach to electric tower detection and classification for power line inspection.
Proceedings of the 2014 International Joint Conference on Neural Networks, 2014


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