What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Robotics
His main research concerns Artificial intelligence, Computer vision, Visual odometry, Pose and Robotics.
His is doing research in Simultaneous localization and mapping, Robot, Mobile robot, Inertial measurement unit and Robustness, both of which are found in Artificial intelligence.
His work in Robot tackles topics such as Human–computer interaction which are related to areas like Metric.
Within one scientific family, he focuses on topics pertaining to Point under Computer vision, and may sometimes address concerns connected to Camera auto-calibration.
In Visual odometry, he works on issues like Image segmentation, which are connected to Robot learning, Contextual image classification, Visual perception and Machine learning.
His studies deal with areas such as Computer security and Real-time computing as well as Robotics.
His most cited work include:
- Introduction to Autonomous Mobile Robots (1519 citations)
- Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (1201 citations)
- SVO: Fast semi-direct monocular visual odometry (981 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Computer vision, Robot, High dynamic range and Trajectory are his primary areas of study.
In the subject of general Artificial intelligence, his work in Visual odometry, Robotics, Odometry and Mobile robot is often linked to Asynchronous communication, thereby combining diverse domains of study.
His work focuses on many connections between Visual odometry and other disciplines, such as Feature extraction, that overlap with his field of interest in Visualization.
His studies in Motion blur, Pose, Motion estimation, Monocular and Feature are all subfields of Computer vision research.
His Robot research incorporates themes from Real-time computing, Task and Key.
His Trajectory research incorporates elements of Tracking and Vehicle dynamics.
He most often published in these fields:
- Artificial intelligence (78.95%)
- Computer vision (61.18%)
- Robot (18.75%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (78.95%)
- Computer vision (61.18%)
- Asynchronous communication (10.20%)
In recent papers he was focusing on the following fields of study:
Davide Scaramuzza mainly focuses on Artificial intelligence, Computer vision, Asynchronous communication, Drone and High dynamic range.
Artificial intelligence is closely attributed to Pattern recognition in his work.
His work on Odometry expands to the thematically related Computer vision.
His study looks at the relationship between Monocular and topics such as Depth perception, which overlap with Robot.
His work in Real-time computing covers topics such as State which are related to areas like Control theory and Trajectory.
The study incorporates disciplines such as Robotics and Reinforcement learning in addition to Trajectory.
Between 2019 and 2021, his most popular works were:
- Event-based Vision: A Survey. (162 citations)
- High Speed and High Dynamic Range Video with an Event Camera (49 citations)
- A General Framework for Uncertainty Estimation in Deep Learning (46 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His scientific interests lie mostly in Artificial intelligence, Computer vision, High dynamic range, Drone and Motion blur.
His study involves Artificial neural network, Convolutional neural network, Robotics, Voxel and Segmentation, a branch of Artificial intelligence.
His work deals with themes such as Probabilistic logic, Deep learning, Robot control, Control theory and Robot, which intersect with Artificial neural network.
The concepts of his Robotics study are interwoven with issues in Feature detection, Pixel, Industrial engineering and Spiking neural network.
His Computer vision study frequently links to adjacent areas such as Odometry.
As a part of the same scientific study, Davide Scaramuzza usually deals with the Motion blur, concentrating on Iterative reconstruction and frequently concerns with Smoothing.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
