San Francisco, California, United States
Contact Info
6K followers
500+ connections
Activity
Experience & Education
Licenses & Certifications
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Cambridge English: Proficiency (C2)
Cambridge English Language Assessment
IssuedCredential ID 0056941302 -
International General Certificate of Secondary Education
University of Cambridge
IssuedCredential ID 0029251578
Publications
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SemanticDepth: Fusing Semantic Segmentation and Monocular Depth Estimation for Enabling Autonomous Driving in Roads without Lane Lines
Sensors (MPDI)
Typically, lane departure warning systems rely on lane lines being present on the road.
However, in many scenarios, e.g., secondary roads or some streets in cities, lane lines are either
not present or not sufficiently well signaled. In this work, we present a vision-based method to
locate a vehicle within the road when no lane lines are present using only RGB images as input.
To this end, we propose to fuse together the outputs of a semantic segmentation and a monocular
depth…Typically, lane departure warning systems rely on lane lines being present on the road.
However, in many scenarios, e.g., secondary roads or some streets in cities, lane lines are either
not present or not sufficiently well signaled. In this work, we present a vision-based method to
locate a vehicle within the road when no lane lines are present using only RGB images as input.
To this end, we propose to fuse together the outputs of a semantic segmentation and a monocular
depth estimation architecture to reconstruct locally a semantic 3D point cloud of the viewed scene.
We only retain points belonging to the road and, additionally, to any kind of fences or walls that
might be present right at the sides of the road. We then compute the width of the road at a certain
point on the planned trajectory and, additionally, what we denote as the fence-to-fence distance.
Our system is suited to any kind of motoring scenario and is especially useful when lane lines are
not present on the road or do not signal the path correctly. The additional fence-to-fence distance
computation is complementary to the road’s width estimation. We quantitatively test our method
on a set of images featuring streets of the city of Munich that contain a road-fence structure, so as
to compare our two proposed variants, namely the road’s width and the fence-to-fence distance
computation. In addition, we also validate our system qualitatively on the Stuttgart sequence of the
publicly available Cityscapes dataset, where no fences or walls are present at the sides of the road,
thus demonstrating that our system can be deployed in a standard city-like environment. For the
benefit of the community, we make our software open sourceOther authorsSee publication -
Robust Visual-Aided Autonomous Takeoff, Tracking, and Landing of a Small UAV on a Moving Landing Platform for Life-Long Operation
Applied Sciences (MDPI)
Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and…
Robot cooperation is key in Search and Rescue (SaR) tasks. Frequently, these tasks take place in complex scenarios affected by different types of disasters, so an aerial viewpoint is useful for autonomous navigation or human tele-operation. In such cases, an Unmanned Aerial Vehicle (UAV) in cooperation with an Unmanned Ground Vehicle (UGV) can provide valuable insight into the area. To carry out its work successfully, such as multi-robot system requires the autonomous takeoff, tracking, and landing of the UAV on the moving UGV. Furthermore, it needs to be robust and capable of life-long operation. In this paper, we present an autonomous system that enables a UAV to take off autonomously from a moving landing platform, locate it using visual cues, follow it, and robustly land on it. The system relies on a finite state machine, which together with a novel re-localization module allows the system to operate robustly for extended periods of time and to recover from potential failed landing maneuvers. Two approaches for tracking and landing are developed, implemented, and tested. The first variant is based on a novel height-adaptive PID controller that uses the current position of the landing platform as the target. The second one combines this height-adaptive PID controller with a Kalman filter in order to predict the future positions of the platform and provide them as input to the PID controller. This facilitates tracking and, mainly, landing. Both the system as a whole and the re-localization module in particular have been tested extensively in a simulated environment (Gazebo). We also present a qualitative evaluation of the system on the real robotic platforms, demonstrating that our system can also be deployed on real robotic platforms. For the benefit of the community, we make our software open source.
Other authorsSee publication
Courses
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2015 Summer School on Mobile Manipulators http://intranet.ceautomatica.es/sites/default/files/upload/10/files/CEA_GTRob_boletin25.pdf
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Honors & Awards
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Postgraduate Scholarship "Becas Postgrado Mutua Madrileña"
Mutua Madrileña
Postgraduate Scholarship awarded to 40 Spanish students for studying a Masters abroad.
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Jumping Talent
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Selected as one of the 96 most promising university students in Spain (out of more than 5.000 candidates) to take part in an event sponsored by 12 top multinational companies.
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Excellence Scholarship
Comunidad de Madrid
Scholarship awarded for an excellent GPA
Languages
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English
Native or bilingual proficiency
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Spanish
Native or bilingual proficiency
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French
Professional working proficiency
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German
Full professional proficiency
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Italian
Professional working proficiency
Organizations
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Robdos Team Underwater Robotics
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- Presenthttp://www.robdosteam.com/
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