What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Mechanical engineering
The scientist’s investigation covers issues in Artificial intelligence, Robot, Computer vision, Mobile robot and Simulation.
His study in Robotics, Inertial measurement unit, Robustness, Feature extraction and Odometry falls under the purview of Artificial intelligence.
In his study, Ground truth is inextricably linked to Algorithm, which falls within the broad field of Robotics.
His work carried out in the field of Robot brings together such families of science as Control engineering, Human–computer interaction and Control theory.
Roland Siegwart has researched Computer vision in several fields, including Kalman filter, Extended Kalman filter, Simultaneous localization and mapping and Visual odometry.
Roland Siegwart has included themes like Collision, Control theory and Quadrupedal robot in his Simulation study.
His most cited work include:
- BRISK: Binary Robust invariant scalable keypoints (2080 citations)
- Introduction to Autonomous Mobile Robots (1519 citations)
- PID vs LQ control techniques applied to an indoor micro quadrotor (883 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Robot, Computer vision, Mobile robot and Control theory.
Roland Siegwart interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence.
His Robot research includes themes of Control engineering, Terrain, Simulation and Human–computer interaction.
His study in the field of Inertial measurement unit, Feature and Feature extraction is also linked to topics like Inertial frame of reference.
His research on Mobile robot frequently connects to adjacent areas such as Kalman filter.
His is doing research in Control theory, Trajectory, Torque and Actuator, both of which are found in Control theory.
He most often published in these fields:
- Artificial intelligence (48.30%)
- Robot (35.43%)
- Computer vision (31.26%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (48.30%)
- Robot (35.43%)
- Computer vision (31.26%)
In recent papers he was focusing on the following fields of study:
Roland Siegwart mostly deals with Artificial intelligence, Robot, Computer vision, Control theory and Representation.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Lidar, Machine learning and Pattern recognition.
His primary area of study in Robot is in the field of Motion planning.
His Computer vision research is multidisciplinary, incorporating elements of Pipeline and Convolutional neural network.
His work in the fields of Control theory, such as Control theory and PID controller, intersects with other areas such as Free flight.
The study incorporates disciplines such as Signed distance function and Structure in addition to Representation.
Between 2019 and 2021, his most popular works were:
- SegMap: Segment-based mapping and localization using data-driven descriptors: (31 citations)
- An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments (21 citations)
- Object Finding in Cluttered Scenes Using Interactive Perception (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Mechanical engineering
- Computer vision
His main research concerns Artificial intelligence, Robot, Computer vision, Motion planning and Robotics.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Task.
His Robot research is multidisciplinary, relying on both Field and Control theory, Control theory.
When carried out as part of a general Computer vision research project, his work on Segmentation, Point cloud and Orientation is frequently linked to work in Code and Inertial frame of reference, therefore connecting diverse disciplines of study.
His research integrates issues of Terrain, Data mining and Human–computer interaction in his study of Motion planning.
His research investigates the connection between Robotics and topics such as Software that intersect with problems in Systems architecture, Extensibility, Track, Control engineering and Formula Student.
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