What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of investigation include Artificial intelligence, Computer vision, Inertial measurement unit, Robot and Initialization.
His research combines State and Computer vision.
His studies in Inertial measurement unit integrate themes in fields like Monocular and Robustness.
His Robustness study combines topics from a wide range of disciplines, such as Mobile device, Augmented reality and Odometry.
While the research belongs to areas of Odometry, he spends his time largely on the problem of Feature extraction, intersecting his research to questions surrounding Computation.
His Robot study integrates concerns from other disciplines, such as Vehicle dynamics and System dynamics.
His most cited work include:
- VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator (848 citations)
- Autonomous multi-floor indoor navigation with a computationally constrained MAV (302 citations)
- Collaborative mapping of an earthquake-damaged building via ground and aerial robots (279 citations)
What are the main themes of his work throughout his whole career to date?
Shaojie Shen mainly investigates Artificial intelligence, Computer vision, Trajectory, Inertial measurement unit and Monocular.
Shaojie Shen integrates many fields, such as Artificial intelligence and Initialization, in his works.
As part of his studies on Computer vision, Shaojie Shen frequently links adjacent subjects like Odometry.
His Trajectory research focuses on Motion planning and how it connects with Euclidean distance and Signed distance function.
His research on Inertial measurement unit also deals with topics like
- State and Structure most often made with reference to Real-time computing,
- Eye tracking most often made with reference to Tracking.
His Monocular study combines topics in areas such as Depth map, Metric, Mobile robot, Pose and Visualization.
He most often published in these fields:
- Artificial intelligence (56.63%)
- Computer vision (48.19%)
- Trajectory (21.69%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (56.63%)
- Computer vision (48.19%)
- Trajectory (21.69%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Computer vision, Trajectory, Real-time computing and Robustness.
His research in Artificial intelligence intersects with topics in Tree and Structure.
With his scientific publications, his incorporates both Computer vision and Graph.
As a part of the same scientific study, he usually deals with the Trajectory, concentrating on Control engineering and frequently concerns with Motion planning and Remotely operated underwater vehicle.
His biological study spans a wide range of topics, including Robot and Swarm behaviour.
Shaojie Shen combines subjects such as Algorithm, Search algorithm and Norm with his study of Robustness.
Between 2019 and 2021, his most popular works were:
- Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments (21 citations)
- Robust Real-time UAV Replanning Using Guided Gradient-based Optimization and Topological Paths (12 citations)
- Decentralized Visual-Inertial-UWB Fusion for Relative State Estimation of Aerial Swarm (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Real-time computing, Trajectory, Artificial intelligence, Motion capture and Ground truth are his primary areas of study.
The various areas that Shaojie Shen examines in his Real-time computing study include Robot and Robustness.
His research integrates issues of Control engineering, Motion and Bundle adjustment in his study of Trajectory.
Many of his studies involve connections with topics such as Computer vision and Artificial intelligence.
When carried out as part of a general Computer vision research project, his work on Pose, Pixel and 3D reconstruction is frequently linked to work in High dynamic range, therefore connecting diverse disciplines of study.
His Motion capture study integrates concerns from other disciplines, such as Swarm behaviour and State.
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