What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Statistics
Yvan Petillot mainly focuses on Artificial intelligence, Computer vision, Sonar, Remotely operated underwater vehicle and Feature extraction.
Yvan Petillot integrates Artificial intelligence with Process in his study.
His Computer vision study frequently draws connections between related disciplines such as Probabilistic logic.
His research in Sonar intersects with topics in Markov random field, Noise and Filter.
His Remotely operated underwater vehicle study integrates concerns from other disciplines, such as Inertial navigation system, Real-time computing, Motion planning and Trajectory.
In his study, which falls under the umbrella issue of Feature extraction, Spatial analysis, Electronic countermeasure and False alarm is strongly linked to Shadow.
His most cited work include:
- Path Planning for Autonomous Underwater Vehicles (324 citations)
- Underwater vehicle obstacle avoidance and path planning using a multi-beam forward looking sonar (187 citations)
- An automatic approach to the detection and extraction of mine features in sidescan sonar (182 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Sonar, Underwater and Remotely operated underwater vehicle.
His research links Pattern recognition with Artificial intelligence.
Computer vision is frequently linked to Side-scan sonar in his study.
His Sonar study combines topics from a wide range of disciplines, such as Contextual image classification, Image registration, MIMO and Filter.
His study in Underwater is interdisciplinary in nature, drawing from both Control engineering, Subsea and Systems engineering.
His Remotely operated underwater vehicle research is multidisciplinary, incorporating perspectives in Obstacle avoidance and Motion planning.
He most often published in these fields:
- Artificial intelligence (52.16%)
- Computer vision (39.22%)
- Sonar (27.45%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (52.16%)
- Robot (10.20%)
- Underwater (21.96%)
In recent papers he was focusing on the following fields of study:
Yvan Petillot focuses on Artificial intelligence, Robot, Underwater, Computer vision and Task.
His Artificial intelligence study combines topics in areas such as Manipulator and Machine learning.
His Robot study incorporates themes from Distributed computing, Robustness and Human–computer interaction.
His Underwater study incorporates themes from Control engineering, Photon counting, 3D reconstruction and Domain.
Yvan Petillot regularly links together related areas like Signal-to-noise ratio in his Computer vision studies.
His research in Sonar intersects with topics in Transfer of learning, MIMO, Remotely operated underwater vehicle and SAFER.
Between 2015 and 2021, his most popular works were:
- StaticFusion: Background Reconstruction for Dense RGB-D SLAM in Dynamic Environments (61 citations)
- Adaptive low-level control of autonomous underwater vehicles using deep reinforcement learning (52 citations)
- Three‐dimensional reconstruction of underwater objects using wide‐aperture imaging SONAR (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Statistics
Yvan Petillot spends much of his time researching Artificial intelligence, Underwater, Robot, Computer vision and Robotics.
His study brings together the fields of Manipulator and Artificial intelligence.
His work on Underwater robotics is typically connected to Environmental monitoring as part of general Underwater study, connecting several disciplines of science.
His Humanoid robot and Search and rescue study in the realm of Robot connects with subjects such as Benchmarking and Competition.
His work carried out in the field of Humanoid robot brings together such families of science as Feature, DUAL, Motion planning and Human–computer interaction.
His research integrates issues of Signal-to-noise ratio and Clutter in his study of Computer vision.
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