What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of study are Artificial intelligence, Mobile robot, Kalman filter, Inertial navigation system and Inertial measurement unit.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Computer vision.
His Simultaneous localization and map building and Topological map study in the realm of Mobile robot connects with subjects such as Graph theory.
Eduardo Nebot studied Kalman filter and Algorithm that intersect with Motion planning.
His Inertial navigation system research incorporates themes from Remotely operated underwater vehicle, Real-time computing, Global Positioning System and Control theory.
His research integrates issues of Embedded system and Receiver autonomous integrity monitoring in his study of Inertial measurement unit.
His most cited work include:
- Optimization of the simultaneous localization and map-building algorithm for real-time implementation (695 citations)
- Consistency of the EKF-SLAM Algorithm (376 citations)
- A high integrity IMU/GPS navigation loop for autonomous land vehicle applications (358 citations)
What are the main themes of his work throughout his whole career to date?
Eduardo Nebot spends much of his time researching Artificial intelligence, Computer vision, Real-time computing, Kalman filter and Global Positioning System.
His studies deal with areas such as Lidar and Machine learning as well as Artificial intelligence.
His work carried out in the field of Computer vision brings together such families of science as Dead reckoning and Representation.
Eduardo Nebot combines subjects such as Intelligent transportation system and Simulation with his study of Real-time computing.
His Kalman filter research is multidisciplinary, incorporating elements of Algorithm and Filter.
His research in Global Positioning System tackles topics such as Navigation system which are related to areas like Inertial navigation system.
He most often published in these fields:
- Artificial intelligence (47.72%)
- Computer vision (28.93%)
- Real-time computing (15.74%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (47.72%)
- Computer vision (28.93%)
- Lidar (7.11%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Artificial intelligence, Computer vision, Lidar, Probabilistic logic and Pedestrian.
His studies in Artificial intelligence integrate themes in fields like Machine learning and Pattern recognition.
His biological study spans a wide range of topics, including Convolutional neural network and Data set.
Eduardo Nebot has researched Lidar in several fields, including Calibration, Coordinate system, Orthophoto, Global Positioning System and Sensor fusion.
In Crowds, Eduardo Nebot works on issues like Mobile robot, which are connected to Feature.
His Feature research incorporates elements of Kalman filter, Feature extraction, Spatial relation and Vehicle tracking system.
Between 2017 and 2021, his most popular works were:
- Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks (43 citations)
- A Recurrent Neural Network Solution for Predicting Driver Intention at Unsignalized Intersections (42 citations)
- Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Computer vision, Trajectory, Recurrent neural network and Segmentation.
Eduardo Nebot has included themes like Lidar, Machine learning and Intelligent transportation system in his Artificial intelligence study.
The various areas that Eduardo Nebot examines in his Machine learning study include Probabilistic logic, Crowds and Mobile robot.
The Projection and Inertial measurement unit research Eduardo Nebot does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Process and Visibility, therefore creating a link between diverse domains of science.
In his study, Data mining and Cluster analysis is strongly linked to Tracking system, which falls under the umbrella field of Recurrent neural network.
His Segmentation research is multidisciplinary, incorporating perspectives in Training set and System integrity.
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