What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
Juan Nieto focuses on Artificial intelligence, Robot, Simultaneous localization and mapping, Computer vision and Pattern recognition.
His Artificial intelligence research focuses on Machine learning and how it relates to Probabilistic logic.
His study in Robot is interdisciplinary in nature, drawing from both Data-driven and Time trajectory.
His research in Simultaneous localization and mapping focuses on subjects like Extended Kalman filter, which are connected to Filter.
His study in the fields of Segmentation and Image segmentation under the domain of Pattern recognition overlaps with other disciplines such as Graph based.
The various areas that he examines in his Kalman filter study include Noise, Algorithm, Gaussian noise and Mobile robot.
His most cited work include:
- Consistency of the EKF-SLAM Algorithm (376 citations)
- From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots (180 citations)
- Consistency of the FastSLAM algorithm (177 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, Motion planning and Pattern recognition.
Artificial intelligence and Machine learning are frequently intertwined in his study.
His work deals with themes such as Simultaneous localization and mapping, Representation and Lidar, which intersect with Computer vision.
He has included themes like Field, Data-driven, Task and Trajectory in his Robot study.
His Motion planning research is multidisciplinary, incorporating perspectives in Data mining and Multirotor.
His Pattern recognition research integrates issues from RGB color model and Point cloud.
He most often published in these fields:
- Artificial intelligence (59.77%)
- Computer vision (31.64%)
- Robot (23.05%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (59.77%)
- Robot (23.05%)
- Computer vision (31.64%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Robot, Computer vision, Motion planning and Trajectory.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Lidar, Machine learning and Pattern recognition.
The concepts of his Robot study are interwoven with issues in Field, Representation, Distributed computing and State.
His research investigates the connection between Computer vision and topics such as Data-driven that intersect with problems in Rendering, Solid modeling and 3D reconstruction.
His work in Motion planning tackles topics such as Data mining which are related to areas like Tree and Hyperparameter optimization.
His Trajectory research incorporates themes from Tracking, Optimization problem and Torque.
Between 2019 and 2021, his most popular works were:
- SegMap: Segment-based mapping and localization using data-driven descriptors: (31 citations)
- An Efficient Sampling-Based Method for Online Informative Path Planning in Unknown Environments (21 citations)
- Object Finding in Cluttered Scenes Using Interactive Perception (12 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Juan Nieto mainly focuses on Artificial intelligence, Robot, Robotics, Computer vision and Trajectory.
His Artificial intelligence study combines topics in areas such as Machine learning and Automotive industry.
In the field of Robot, his study on Motion planning overlaps with subjects such as Sustainability.
His Robotics research focuses on Task and how it connects with Automation, Human–computer interaction and Object.
His Computer vision study integrates concerns from other disciplines, such as Task analysis and Heuristics.
His Trajectory research incorporates elements of Smoothing, Industrial inspection, Vision based and Computational science.
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