What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Programming language
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 most cited work include:
- ORB: An efficient alternative to SIFT or SURF (4594 citations)
- G 2 o: A general framework for graph optimization (1464 citations)
- Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes (636 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (66.08%)
- Computer vision (36.84%)
- Robot (28.07%)
What were the highlights of his more recent work (between 2011-2021)?
- Artificial intelligence (66.08%)
- Computer vision (36.84%)
- Object (8.77%)
In recent papers he was focusing on the following fields of study:
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
- Pipeline that connect with fields like Depth map,
- Pose that intertwine with fields like Object detection,
- Texture which is related to area like Tracking.
His Robot study integrates concerns from other disciplines, such as Real-time computing and Perception.
He has researched Robot manipulator in several fields, including Virtual machine, Deep-sky object, Control engineering, Method and Robot end effector.
Between 2011 and 2021, his most popular works were:
- Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes (636 citations)
- Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping (325 citations)
- Keyframe-based Visual-Inertial SLAM using Nonlinear Optimization (173 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Programming language
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.
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.
