What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Salah Sukkarieh mainly focuses on Artificial intelligence, Computer vision, Inertial measurement unit, Inertial navigation system and Simultaneous localization and mapping.
As part of his studies on Artificial intelligence, Salah Sukkarieh often connects relevant subjects like Machine learning.
The Computer vision study combines topics in areas such as Robot, Terrain and Bearing.
The various areas that Salah Sukkarieh examines in his Inertial measurement unit study include Kalman filter, Observability, Control engineering and Monocular vision.
The concepts of his Inertial navigation system study are interwoven with issues in Real-time computing, Navigation system, Simulation and Sensor fusion.
His study in Simultaneous localization and mapping is interdisciplinary in nature, drawing from both Distributed computing and Flight test.
His most cited work include:
- A high integrity IMU/GPS navigation loop for autonomous land vehicle applications (358 citations)
- The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications (335 citations)
- Visual-Inertial-Aided Navigation for High-Dynamic Motion in Built Environments Without Initial Conditions (257 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Computer vision, Motion planning, Real-time computing and Robot.
His Artificial intelligence study incorporates themes from Machine learning, Terrain and Pattern recognition.
His Computer vision study combines topics from a wide range of disciplines, such as Remotely operated underwater vehicle and Inertial navigation system.
The study incorporates disciplines such as Kalman filter, Control theory and Navigation system in addition to Inertial navigation system.
Salah Sukkarieh interconnects Simulation and Sensor fusion in the investigation of issues within Real-time computing.
His study deals with a combination of Inertial frame of reference and Inertial measurement unit.
He most often published in these fields:
- Artificial intelligence (39.18%)
- Computer vision (19.59%)
- Motion planning (14.09%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (39.18%)
- Robot (13.40%)
- Robotics (9.97%)
In recent papers he was focusing on the following fields of study:
Salah Sukkarieh spends much of his time researching Artificial intelligence, Robot, Robotics, Motion planning and Control theory.
His work deals with themes such as Monte Carlo tree search, Machine learning, Field and Computer vision, which intersect with Artificial intelligence.
Salah Sukkarieh has researched Computer vision in several fields, including Sampling, Resolution, Data set and Multimodal data.
His study looks at the relationship between Robot and fields such as Terrain, as well as how they intersect with chemical problems.
In his study, which falls under the umbrella issue of Motion planning, Planner is strongly linked to Motion control.
His studies in Control theory integrate themes in fields like Slip, Estimator, Work and Mathematical model.
Between 2016 and 2021, his most popular works were:
- Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review (202 citations)
- Cattle segmentation and contour extraction based on Mask R-CNN for precision livestock farming (27 citations)
- Geometric Priors for Gaussian Process Implicit Surfaces (26 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Salah Sukkarieh mostly deals with Artificial intelligence, Robot, Control theory, Estimator and Computer vision.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
His Robot study also includes fields such as
- Recurrent neural network, which have a strong connection to Trajectory,
- Monte Carlo tree search which is related to area like Obstacle avoidance.
As part of the same scientific family, Salah Sukkarieh usually focuses on Control theory, concentrating on Slip and intersecting with Mobile robot, Estimation theory, Control and Compensation.
The Estimator study combines topics in areas such as Estimation, Moving horizon estimation and Quadratic cost.
The concepts of his Computer vision study are interwoven with issues in Sampling, Data set, Resolution and Multimodal data.
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