2023 - Research.com Computer Science in Switzerland Leader Award
2023 - Research.com Mechanical and Aerospace Engineering in Switzerland Leader Award
2023 - Research.com Electronics and Electrical Engineering in Switzerland Leader Award
2022 - Research.com Computer Science in Switzerland Leader Award
2022 - Research.com Mechanical and Aerospace Engineering in Switzerland Leader Award
2022 - Research.com Electronics and Electrical Engineering in Switzerland Leader Award
2008 - IEEE Fellow For contributions to mobile, networked, and micro-scale robots
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 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.
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.
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.
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.
Introduction to Autonomous Mobile Robots
Roland Siegwart;Illah R. Nourbakhsh;Davide Scaramuzza.
BRISK: Binary Robust invariant scalable keypoints
Stefan Leutenegger;Margarita Chli;Roland Y. Siegwart.
international conference on computer vision (2011)
PID vs LQ control techniques applied to an indoor micro quadrotor
S. Bouabdallah;A. Noth;R. Siegwart.
intelligent robots and systems (2004)
Keyframe-based visual-inertial odometry using nonlinear optimization
Stefan Leutenegger;Simon Lynen;Michael Bosse;Roland Siegwart.
The International Journal of Robotics Research (2015)
Backstepping and Sliding-mode Techniques Applied to an Indoor Micro Quadrotor
S. Bouabdallah;R. Siegwart.
international conference on robotics and automation (2005)
Design and control of an indoor micro quadrotor
S. Bouabdallah;P. Murrieri;R. Siegwart.
international conference on robotics and automation (2004)
The EuRoC micro aerial vehicle datasets
Michael Burri;Janosch Nikolic;Pascal Gohl;Thomas Schneider.
The International Journal of Robotics Research (2016)
Full control of a quadrotor
S. Bouabdallah;R. Siegwart.
intelligent robots and systems (2007)
A Toolbox for Easily Calibrating Omnidirectional Cameras
Davide Scaramuzza;Agostino Martinelli;Roland Siegwart.
intelligent robots and systems (2006)
Comparing ICP variants on real-world data sets
François Pomerleau;Francis Colas;Roland Siegwart;Stéphane Magnenat.
Autonomous Robots (2013)
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