1995 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For landmark contributions to nonmonotonic logic, reasoning about beliefs, and mobile robotics.
Kurt Konolige focuses on Artificial intelligence, Computer vision, Mobile robot, Robot and Simultaneous localization and mapping. His study in the fields of Pose, Scale-invariant feature transform and Structure from motion under the domain of Artificial intelligence overlaps with other disciplines such as Matching. His studies in Scale-invariant feature transform integrate themes in fields like Invariant and 3D single-object recognition.
His Computer vision research incorporates themes from Visual odometry and Robustness. His studies deal with areas such as Algorithm, Robotics and Bundle adjustment as well as Simultaneous localization and mapping. In his research on the topic of Object detection, Cognitive neuroscience of visual object recognition is strongly related with Pattern recognition.
His primary areas of investigation include Artificial intelligence, Computer vision, Robot, Mobile robot and Non-monotonic logic. His Artificial intelligence research integrates issues from Machine learning and Natural language processing. His work in Computer vision addresses subjects such as Visual odometry, which are connected to disciplines such as Motion estimation.
His Robot research is multidisciplinary, incorporating perspectives in Hidden Markov model, Real-time computing, Bayesian probability and Human–computer interaction. Kurt Konolige interconnects Control engineering, Control theory, Control and Motion planning in the investigation of issues within Mobile robot. His work deals with themes such as Autoepistemic logic, Default logic, Cognitive science and Deductive reasoning, which intersect with Non-monotonic logic.
Artificial intelligence, Computer vision, Object, Robot and Robot manipulator are his primary areas of study. His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition. His study in Computer vision is interdisciplinary in nature, drawing from both Robotics and GRASP.
His Object study also includes
His primary scientific interests are in Artificial intelligence, Computer vision, Object, Robot and Computer graphics. His Artificial intelligence research is multidisciplinary, incorporating elements of Python and Pattern recognition. His Computer vision study incorporates themes from Robotics and GRASP.
His Object research includes elements of Texture, Pose and Robot manipulator. The study incorporates disciplines such as Real-time computing and Bundle adjustment in addition to Robot. His work in the fields of Computer graphics, such as Stereo cameras, intersects with other areas such as Geography and Virtual representation.
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ORB: An efficient alternative to SIFT or SURF
Ethan Rublee;Vincent Rabaud;Kurt Konolige;Gary Bradski.
international conference on computer vision (2011)
G 2 o: A general framework for graph optimization
Rainer Kummerle;Giorgio Grisetti;Hauke Strasdat;Kurt Konolige.
international conference on robotics and automation (2011)
Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes
Stefan Hinterstoisser;Vincent Lepetit;Slobodan Ilic;Stefan Holzer.
asian conference on computer vision (2012)
CenSurE: Center Surround Extremas for Realtime Feature Detection and Matching
Motilal Agrawal;Kurt Konolige;Morten Rufus Blas.
european conference on computer vision (2008)
Small Vision Systems: Hardware and Implementation
Incremental mapping of large cyclic environments
J.-S. Gutmann;K. Konolige.
computational intelligence in robotics and automation (1999)
FrameSLAM: From Bundle Adjustment to Real-Time Visual Mapping
K. Konolige;M. Agrawal.
IEEE Transactions on Robotics (2008)
The Office Marathon: Robust navigation in an indoor office environment
Eitan Marder-Eppstein;Eric Berger;Tully Foote;Brian Gerkey.
international conference on robotics and automation (2010)
A deduction model of belief
Large-Scale Visual Odometry for Rough Terrain
Kurt Konolige;Motilal Agrawal;Joan Solà.
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