What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Algorithm
- Computer vision
His main research concerns Simultaneous localization and mapping, Artificial intelligence, Robot, Robustness and Mathematical optimization.
His Artificial intelligence study frequently draws connections to adjacent fields such as Computer vision.
When carried out as part of a general Robot research project, his work on Robotics and Mobile robot is frequently linked to work in Domain, therefore connecting diverse disciplines of study.
His research in Robotics intersects with topics in Inference, Metric, Model predictive control and Human–computer interaction.
His study looks at the relationship between Robustness and topics such as Optimization problem, which overlap with Conditional independence and Theoretical computer science.
In general Mathematical optimization study, his work on Quadratic programming often relates to the realm of Event, thereby connecting several areas of interest.
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)
- On-Manifold Preintegration for Real-Time Visual--Inertial Odometry (424 citations)
What are the main themes of his work throughout his whole career to date?
The scientist’s investigation covers issues in Robot, Artificial intelligence, Algorithm, Mathematical optimization and Simultaneous localization and mapping.
His studies deal with areas such as Object and Real-time computing as well as Robot.
His Artificial intelligence research is multidisciplinary, incorporating elements of Machine learning, Metric and Computer vision.
His Algorithm research includes elements of Point cloud, Rotation, Outlier, Relaxation and Solver.
His research investigates the connection with Mathematical optimization and areas like Nonlinear programming which intersect with concerns in Inertial measurement unit.
His studies deal with areas such as Graphical model, Extended Kalman filter, Linear approximation and Trajectory as well as Simultaneous localization and mapping.
He most often published in these fields:
- Robot (32.91%)
- Artificial intelligence (29.75%)
- Algorithm (27.22%)
What were the highlights of his more recent work (between 2019-2021)?
- Robustness (15.19%)
- Artificial intelligence (29.75%)
- Robot (32.91%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Robustness, Artificial intelligence, Robot, Algorithm and Outlier.
His study in Robustness is interdisciplinary in nature, drawing from both Multi sensor and Pose.
In the subject of general Artificial intelligence, his work in Deep learning and Robotics is often linked to Polygon mesh, thereby combining diverse domains of study.
The study incorporates disciplines such as Object, Real-time computing and Representation in addition to Robot.
Luca Carlone has included themes like Geometric data analysis, Rotation, Relaxation, Convolution and Solver in his Algorithm study.
His work deals with themes such as Simultaneous localization and mapping, Trajectory, Odometry and Metric, which intersect with Computer vision.
Between 2019 and 2021, his most popular works were:
- TEASER: Fast and Certifiable Point Cloud Registration (60 citations)
- Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping (53 citations)
- Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Algorithm
- Computer vision
His scientific interests lie mostly in Outlier, Robustness, Relaxation, Algorithm and Simultaneous localization and mapping.
His Robustness research is within the category of Artificial intelligence.
His research in Relaxation intersects with topics in RANSAC, Rotation, Time complexity, Function and Optimization problem.
His Simultaneous localization and mapping research entails a greater understanding of Robot.
His Trajectory study combines topics from a wide range of disciplines, such as Visualization, Distributed computing and Computer vision.
His Odometry research is multidisciplinary, relying on both Salient and Real-time computing.
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