What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer vision
Shoudong Huang spends much of his time researching Simultaneous localization and mapping, Extended Kalman filter, Control theory, Artificial intelligence and Mathematical optimization.
His Simultaneous localization and mapping research is multidisciplinary, relying on both Optimization problem and Topology.
His Extended Kalman filter study is associated with Kalman filter.
Within one scientific family, Shoudong Huang focuses on topics pertaining to Motion planning under Control theory, and may sometimes address concerns connected to Upper and lower bounds and Model predictive control.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Control engineering, Observability, Computer vision and Pattern recognition.
The Mathematical optimization study combines topics in areas such as Algorithm, Linear least squares, Nonlinear programming and Non-linear least squares.
His most cited work include:
- Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM (292 citations)
- H∞ model reduction for linear time-delay systems: Continuous-time case (94 citations)
- Sparse Local Submap Joining Filter for Building Large-Scale Maps (84 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Artificial intelligence, Simultaneous localization and mapping, Computer vision, Algorithm and Control theory.
His work on Robot and Mobile robot as part of general Artificial intelligence research is often related to Inertial frame of reference, thus linking different fields of science.
His Simultaneous localization and mapping research incorporates themes from Fisher information, Mathematical optimization, Feature and Extended Kalman filter.
His research in Computer vision intersects with topics in Lidar and Odometry.
His Linear least squares study in the realm of Algorithm connects with subjects such as Convergence.
His Control theory research is multidisciplinary, incorporating elements of Control engineering and Model predictive control.
He most often published in these fields:
- Artificial intelligence (37.88%)
- Simultaneous localization and mapping (34.85%)
- Computer vision (28.79%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (37.88%)
- Simultaneous localization and mapping (34.85%)
- Algorithm (22.73%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Simultaneous localization and mapping, Algorithm, Computer vision and Graph.
His study in the field of Pose, Robot and Deep learning is also linked to topics like Inertial frame of reference.
His Simultaneous localization and mapping study incorporates themes from Machine learning, Feature and Motion planning.
His studies deal with areas such as Solver, Position and Spanning tree as well as Algorithm.
The various areas that he examines in his Computer vision study include Lidar and Odometry.
His Graph study combines topics in areas such as Fisher information and Graph.
Between 2018 and 2021, his most popular works were:
- Topological local-metric framework for mobile robots navigation: a long term perspective (28 citations)
- 3D LiDAR-Based Global Localization Using Siamese Neural Network (19 citations)
- Online Estimation of Ocean Current from Sparse GPS Data for Underwater Vehicles (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer vision
Shoudong Huang mainly investigates Artificial intelligence, Inertial frame of reference, Algorithm, Point cloud and Lidar.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Computer vision.
The concepts of his Computer vision study are interwoven with issues in Robot, Feature learning and Benchmark.
Shoudong Huang interconnects Position, Sensor fusion and Interval in the investigation of issues within Algorithm.
His work deals with themes such as Data mining, Feature and Solid modeling, which intersect with Point cloud.
He combines subjects such as Random forest, Representation and Trajectory with his study of Lidar.
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.
