What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of investigation include Artificial intelligence, Mobile robot, Computer vision, Robot and Simultaneous localization and mapping.
As part of his studies on Artificial intelligence, he often connects relevant subjects like Machine learning.
His research in Mobile robot intersects with topics in Discriminative model, Motion planning and AdaBoost.
Patric Jensfelt has researched Computer vision in several fields, including Mobile robot navigation and Kalman filter.
His Robot research includes themes of Commonsense knowledge, Human–computer interaction and Pattern recognition.
He focuses mostly in the field of Simultaneous localization and mapping, narrowing it down to topics relating to Representation and, in certain cases, Subspace topology and Range.
His most cited work include:
- Active global localization for a mobile robot using multiple hypothesis tracking (308 citations)
- Conceptual spatial representations for indoor mobile robots (255 citations)
- Large-scale semantic mapping and reasoning with heterogeneous modalities (191 citations)
What are the main themes of his work throughout his whole career to date?
Patric Jensfelt focuses on Artificial intelligence, Robot, Computer vision, Mobile robot and Robotics.
His biological study spans a wide range of topics, including Machine learning and Human–computer interaction.
His Robot research is multidisciplinary, incorporating perspectives in Point cloud and Spatial relation.
The Cognitive neuroscience of visual object recognition, Tracking and Feature research Patric Jensfelt does as part of his general Computer vision study is frequently linked to other disciplines of science, such as Position and Laser scanning, therefore creating a link between diverse domains of science.
His Mobile robot study combines topics in areas such as Kalman filter and Motion planning.
Patric Jensfelt has included themes like Matching and Field in his Robotics study.
He most often published in these fields:
- Artificial intelligence (75.88%)
- Robot (42.94%)
- Computer vision (42.35%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (75.88%)
- Robot (42.94%)
- Computer vision (42.35%)
In recent papers he was focusing on the following fields of study:
Patric Jensfelt spends much of his time researching Artificial intelligence, Robot, Computer vision, Robotics and Human–computer interaction.
Mobile robot, Deep learning, Object detection, Object and Feature are the subjects of his Artificial intelligence studies.
His work on Social robot as part of general Mobile robot study is frequently linked to Scale, therefore connecting diverse disciplines of science.
His work on Mobile robot navigation as part of general Robot study is frequently connected to Task analysis, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His studies in Computer vision integrate themes in fields like Probabilistic logic, Visual odometry and Convolutional neural network.
His Human–computer interaction research incorporates elements of Control, Service and Reinforcement learning.
Between 2015 and 2021, his most popular works were:
- The STRANDS Project: Long-Term Autonomy in Everyday Environments (112 citations)
- Robot task planning and explanation in open and uncertain worlds (85 citations)
- Autonomous Learning of Object Models on a Mobile Robot (50 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
His primary areas of study are Artificial intelligence, Robot, Robotics, Computer vision and Mobile robot.
His work blends Artificial intelligence and Terrain studies together.
His work carried out in the field of Robot brings together such families of science as Object detection and Benchmark.
He works mostly in the field of Robotics, limiting it down to topics relating to Human–computer interaction and, in certain cases, Control, End user and Service, as a part of the same area of interest.
His Computer vision study integrates concerns from other disciplines, such as Representation, Visual odometry and Convolutional neural network.
His Mobile robot study incorporates themes from Distributed computing and Motion planning.
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