Demo Data from EDBT 2026 / reviewers in the wild / expert
Eric K. Wang 0002
dblp:246/7733 · also Eric Kevin Wang
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0002-9124-7951ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Robot navigation and mapping · 54% Legged, aerial and field robots · 46% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › state estimation › kinematic state estimation
attitude estimation |
0.4 | 1 | 2019 | Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019 |
Robotics › Legged, aerial and field robots › hopping robot
hopping robot control |
0.4 | 1 | 2019 | Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019 |
Robotics › Legged, aerial and field robots
legged robots |
0.4 | 1 | 2019 | Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019 |
Robotics › Robot navigation and mapping
state estimation |
0.4 | 1 | 2019 | Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019 |
Robotics › Robot navigation and mapping › localization
dead reckoning |
0.1 | 1 | 2019 | Drift-free Roll and Pitch Estimation for High-acceleration Hopping · ICRA 2019 |
Methods — techniques the papers use, named apart from their topics
rate gyroscope integration · 0.4encoder-based velocity estimation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | Drift-free Roll and Pitch Estimation for High-acceleration HoppingabstractWe develop a drift-free roll and pitch attitude estimation scheme for monopedal jumping robots. The estimator uses only onboard rate gyroscopes and encoders and does not rely on external sensing or processing. It is capable of recovering from attitude estimate disturbances and, together with onboard velocity estimation, enables fully autonomous stable hopping control. The estimator performs well on a small untethered robot capable of large jumps and extreme stance accelerations. We demonstrate that the robot can follow a rectangular path using onboard dead-reckoning with less than 2 meters of drift over 200 seconds and 300 jumps covering 60 m. We also demonstrate that the robot can operate untethered outdoors under human wireless joystick direction. Justin K. Yim, Eric K. Wang 0002, Ronald S. Fearing |
ICRA | 2 |