What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Algorithm
His main research concerns Artificial intelligence, Computer vision, Simultaneous localization and mapping, Extended Kalman filter and Kalman filter.
The various areas that José María Martínez Montiel examines in his Artificial intelligence study include Point and Filter.
José María Martínez Montiel has included themes like Parametrization and Visual odometry in his Computer vision study.
His studies in Simultaneous localization and mapping integrate themes in fields like Real-time computing and Cloud robotics.
He interconnects Object, Cognitive neuroscience of visual object recognition, Monocular camera and Visual appearance in the investigation of issues within Kalman filter.
His Monocular research incorporates themes from Image sensor and Stereo cameras.
His most cited work include:
- ORB-SLAM: A Versatile and Accurate Monocular SLAM System (2864 citations)
- Inverse Depth Parametrization for Monocular SLAM (615 citations)
- Scale Drift-Aware Large Scale Monocular SLAM (348 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Computer vision, Monocular, Simultaneous localization and mapping and Extended Kalman filter.
His study in Artificial intelligence concentrates on Structure from motion, Bundle adjustment, Robotics, Visual odometry and RGB color model.
His Computer vision study incorporates themes from Kalman filter, Robot, Mobile robot and Robustness.
His Monocular research integrates issues from Object, Feature extraction and Monocular vision.
The study incorporates disciplines such as Visualization, Real-time computing and Pattern recognition in addition to Simultaneous localization and mapping.
His Extended Kalman filter research includes elements of Parametrization, Algorithm and RANSAC, Feature.
He most often published in these fields:
- Artificial intelligence (84.88%)
- Computer vision (82.56%)
- Monocular (31.40%)
What were the highlights of his more recent work (between 2018-2021)?
- Computer vision (82.56%)
- Artificial intelligence (84.88%)
- Monocular (31.40%)
In recent papers he was focusing on the following fields of study:
José María Martínez Montiel focuses on Computer vision, Artificial intelligence, Monocular, Robustness and Bundle adjustment.
José María Martínez Montiel regularly ties together related areas like Simultaneous localization and mapping in his Computer vision studies.
His work on Representation as part of general Artificial intelligence research is frequently linked to Code and Pipeline, thereby connecting diverse disciplines of science.
His Robustness research is multidisciplinary, incorporating perspectives in Global Map and Least squares.
His Bundle adjustment research incorporates elements of Algorithm and Monocular slam.
His work in Parallax covers topics such as Visual odometry which are related to areas like Boosting.
Between 2018 and 2021, his most popular works were:
- ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM (45 citations)
- Live Tracking and Dense Reconstruction for Handheld Monocular Endoscopy (31 citations)
- DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences (11 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Algorithm
His primary areas of study are Monocular, Artificial intelligence, Computer vision, Simultaneous localization and mapping and Bundle adjustment.
His research ties Visual odometry and Computer vision together.
In his research, José María Martínez Montiel undertakes multidisciplinary study on Visual odometry and Initialization.
His Iterative reconstruction research is multidisciplinary, relying on both RGB color model, Augmented reality and Feature.
He applies his multidisciplinary studies on Direct method and Robustness in his research.
José María Martínez Montiel incorporates Inertial measurement unit and Inertial frame of reference in his studies.
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