What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Machine learning
His main research concerns Artificial intelligence, Robot, Robustness, Computer vision and Simultaneous localization and mapping.
His biological study deals with issues like Pattern recognition, which deal with fields such as Minimum spanning tree.
His research in Robot intersects with topics in Data-driven and Human–computer interaction.
The study incorporates disciplines such as Point cloud and Search and rescue in addition to Robustness.
His Stereo cameras, Smoothing, Bundle adjustment and Coordinate system study in the realm of Computer vision interacts with subjects such as Scale.
His Robotics study integrates concerns from other disciplines, such as Coarse to fine, Machine learning, Deep learning and Augmented reality.
His most cited work include:
- Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (1201 citations)
- Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age (455 citations)
- From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots (180 citations)
What are the main themes of his work throughout his whole career to date?
Cesar Cadena mostly deals with Artificial intelligence, Computer vision, Robot, Segmentation and Machine learning.
In his study, Pixel is strongly linked to Pattern recognition, which falls under the umbrella field of Artificial intelligence.
His Computer vision research integrates issues from Simultaneous localization and mapping and Task.
His research integrates issues of Kalman filter, Information filtering system and Human–computer interaction in his study of Simultaneous localization and mapping.
His Search and rescue and Motion planning study, which is part of a larger body of work in Robot, is frequently linked to Metric, bridging the gap between disciplines.
Within one scientific family, he focuses on topics pertaining to Pose under Robotics, and may sometimes address concerns connected to Leverage.
He most often published in these fields:
- Artificial intelligence (78.40%)
- Computer vision (44.80%)
- Robot (31.20%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (78.40%)
- Computer vision (44.80%)
- Segmentation (21.60%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Computer vision, Segmentation, Robot and Key are his primary areas of study.
His studies deal with areas such as Machine learning and Pattern recognition as well as Artificial intelligence.
In the field of Computer vision, his study on Object overlaps with subjects such as Code.
Cesar Cadena has researched Segmentation in several fields, including Pixel, Anomaly detection and Image.
His biological study spans a wide range of topics, including Distributed computing and State.
His research investigates the connection between Distributed computing and topics such as Task analysis that intersect with problems in Global Map, Odometry and Simultaneous localization and mapping.
Between 2019 and 2021, his most popular works were:
- SegMap: Segment-based mapping and localization using data-driven descriptors: (31 citations)
- Voxgraph: Globally Consistent, Volumetric Mapping Using Signed Distance Function Submaps (11 citations)
- Depth Based Semantic Scene Completion With Position Importance Aware Loss (9 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Machine learning
Cesar Cadena focuses on Artificial intelligence, Computer vision, Segmentation, Key and Representation.
His study in Benchmark, Robotics, Sensor fusion, Simultaneous localization and mapping and Artificial neural network is carried out as part of his studies in Artificial intelligence.
His work deals with themes such as Real-time computing, Data-driven, Autoencoder and Global Map, which intersect with Robotics.
The concepts of his Computer vision study are interwoven with issues in Robot and Task.
His Robot research is multidisciplinary, incorporating perspectives in 3D reconstruction and Convolutional neural network.
As part of one scientific family, he deals mainly with the area of Segmentation, narrowing it down to issues related to the Deep learning, and often Orb, Inpainting, Hallucinating and Visual odometry.
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