Demo Data from EDBT 2026 / reviewers in the wild / expert
Lionel Hertig
dblp:135/8647
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2013
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author
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 · 100% |
Topics — the 4 heaviest of 5, 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 |
0.2 | 1 | 2013 | Unified state estimation for a ballbot · ICRA 2013 |
Robotics › Robot navigation and mapping
state estimation |
0.2 | 1 | 2013 | Unified state estimation for a ballbot · ICRA 2013 |
Robotics › Robot navigation and mapping › state estimation › kinematic state estimation
velocity estimation |
0.2 | 1 | 2013 | Unified state estimation for a ballbot · ICRA 2013 |
Robotics › Robot navigation and mapping
localization |
0.0 | 1 | 2013 | Unified state estimation for a ballbot · ICRA 2013 |
Methods — techniques the papers use, named apart from their topics
kinematic modeling · 0.2extended kalman filter · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2013 | Unified state estimation for a ballbotabstractThis paper presents a method for state estimation on a ballbot; i.e., a robot balancing on a single sphere. Within the framework of an extended Kalman filter and by utilizing a complete kinematic model of the robot, sensory information from different sources is combined and fused to obtain accurate estimates of the robot's attitude, velocity, and position. This information is to be used for state feedback control of the dynamically unstable system. Three incremental encoders (attached to the omniwheels that drive the ball of the robot) as well as three rate gyroscopes and accelerometers (attached to the robot's main body) are used as sensors. For the presented method, observability is proven analytically for all essential states in the system, and the algorithm is experimentally evaluated on the Ballbot Rezero. Lionel Hertig, Dominik Schindler, Michael Bloesch, C. David Remy, Roland Siegwart |
ICRA | 1 |