D-Index & Metrics Best Publications

Paul Furgale

ETH Zurich
Switzerland

D-Index & Metrics D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines.

Discipline name Citations Publications World Ranking National Ranking

Computer Science D-index 32 Citations 5,615 67 World Ranking 9132 National Ranking 169

Overview

What is he best known for?

The fields of study he is best known for:

Artificial intelligence
Computer vision
Algorithm

His primary scientific interests are in Artificial intelligence, Computer vision, Inertial measurement unit, Sensor fusion and Simultaneous localization and mapping. As part of his studies on Artificial intelligence, he frequently links adjacent subjects like Algorithm. His studies in Computer vision integrate themes in fields like Mars sample return, Computer graphics and Blind spot.

The concepts of his Inertial measurement unit study are interwoven with issues in Real-time computing, Robotics and Robustness. His Simultaneous localization and mapping research includes elements of Mathematical optimization, Probabilistic logic, Nonlinear programming and Rendering. Paul Furgale has researched Nonlinear programming in several fields, including Kalman filter, Visual odometry, Odometry and Bundle adjustment.

His most cited work include:

Keyframe-based visual-inertial odometry using nonlinear optimization (810 citations)
Unified temporal and spatial calibration for multi-sensor systems (298 citations)
Visual teach and repeat for long-range rover autonomy (198 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, Visual odometry, Simultaneous localization and mapping and Robot. His Artificial intelligence study typically links adjacent topics like Algorithm. Particularly relevant to Stereo camera is his body of work in Computer vision.

His biological study spans a wide range of topics, including Visual navigation, Planetary exploration, Mars Exploration Program, Bundle adjustment and Remote sensing. The Simultaneous localization and mapping study combines topics in areas such as Probabilistic logic, Nonlinear programming and Mathematical optimization. His Robot research includes elements of Representation and Landmark.

He most often published in these fields:

Artificial intelligence (60.61%)
Computer vision (46.97%)
Visual odometry (21.21%)

What were the highlights of his more recent work (between 2016-2017)?

Artificial intelligence (60.61%)
Computer vision (46.97%)
Obstacle (3.03%)

In recent papers he was focusing on the following fields of study:

His primary areas of study are Artificial intelligence, Computer vision, Obstacle, Visual perception and Pipeline. His Point cloud study in the realm of Artificial intelligence connects with subjects such as Pipeline transport. His Point cloud research includes themes of Robot, Motion planning and Trajectory optimization.

Obstacle is connected with Camera resectioning, Blind spot and Depth map in his research.

Between 2016 and 2017, his most popular works were:

3d visual perception for self-driving cars using a multi-camera system: Calibration, mapping, localization, and obstacle detection (76 citations)
Driving on Point Clouds: Motion Planning, Trajectory Optimization, and Terrain Assessment in Generic Nonplanar Environments (43 citations)
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection (6 citations)

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Keyframe-based visual-inertial odometry using nonlinear optimization

Stefan Leutenegger;Simon Lynen;Michael Bosse;Roland Siegwart.
The International Journal of Robotics Research (2015)

1526 Citations

Unified temporal and spatial calibration for multi-sensor systems

Paul Furgale;Joern Rehder;Roland Siegwart.
intelligent robots and systems (2013)

515 Citations

Visual teach and repeat for long-range rover autonomy

Paul Furgale;Timothy D. Barfoot.
Journal of Field Robotics (2010)

313 Citations

A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM

Janosch Nikolic;Joern Rehder;Michael Burri;Pascal Gohl.
international conference on robotics and automation (2014)

282 Citations

Keyframe-based Visual-Inertial SLAM using Nonlinear Optimization

Stefan Leutenegger;Paul Timothy Furgale;Vincent Rabaud;Margarita Chli.
robotics science and systems (2013)

277 Citations

Associating Uncertainty With Three-Dimensional Poses for Use in Estimation Problems

Timothy D. Barfoot;Paul T. Furgale.
IEEE Transactions on Robotics (2014)

246 Citations

Continuous-time batch estimation using temporal basis functions

Paul Furgale;Timothy D. Barfoot;Gabe Sibley.
international conference on robotics and automation (2012)

192 Citations

Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project

Paul Furgale;Ulrich Schwesinger;Martin Rufli;Wojciech Derendarz.
ieee intelligent vehicles symposium (2013)

143 Citations

OpenGV: A unified and generalized approach to real-time calibrated geometric vision

Laurent Kneip;Paul Timothy Furgale.
international conference on robotics and automation (2014)

139 Citations

3d visual perception for self-driving cars using a multi-camera system: Calibration, mapping, localization, and obstacle detection

Christian Häne;Lionel Heng;Gim Hee Lee;Friedrich Fraundorfer.
Image and Vision Computing (2017)

136 Citations

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Frequent Co-Authors

External Links

Personal Website for Paul Furgale