Demo Data from SIGIR 2026 / reviewers in the wild / expert
David Tedaldi
dblp:151/9523
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2014
—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 › sensor calibration
inertial measurement unit calibration |
0.2 | 1 | 2014 | A robust and easy to implement method for IMU calibration without external equipments · ICRA 2014 |
Robotics › Robot navigation and mapping
sensor calibration |
0.2 | 1 | 2014 | A robust and easy to implement method for IMU calibration without external equipments · ICRA 2014 |
Robotics › Robot navigation and mapping › state estimation › kinematic state estimation
attitude estimation |
0.1 | 1 | 2014 | A robust and easy to implement method for IMU calibration without external equipments · ICRA 2014 |
Robotics › Robot navigation and mapping
state estimation |
0.1 | 1 | 2014 | A robust and easy to implement method for IMU calibration without external equipments · ICRA 2014 |
Methods — techniques the papers use, named apart from their topics
static interval detection · 0.2numerical integration · 0.2multi-position calibration · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | A robust and easy to implement method for IMU calibration without external equipmentsabstractMotion sensors as inertial measurement units (IMU) are widely used in robotics, for instance in the navigation and mapping tasks. Nowadays, many low cost micro electro mechanical systems (MEMS) based IMU are available off the shelf, while smartphones and similar devices are almost always equipped with low-cost embedded IMU sensors. Nevertheless, low cost IMUs are affected by systematic error given by imprecise scaling factors and axes misalignments that decrease accuracy in the position and attitudes estimation. In this paper, we propose a robust and easy to implement method to calibrate an IMU without any external equipment. The procedure is based on a multi-position scheme, providing scale and misalignments factors for both the accelerometers and gyroscopes triads, while estimating the sensor biases. Our method only requires the sensor to be moved by hand and placed in a set of different, static positions (attitudes). We describe a robust and quick calibration protocol that exploits an effective parameterless static filter to reliably detect the static intervals in the sensor measurements, where we assume local stability of the gravity's magnitude and stable temperature. We first calibrate the accelerometers triad taking measurement samples in the static intervals. We then exploit these results to calibrate the gyroscopes, employing a robust numerical integration technique. The performances of the proposed calibration technique has been successfully evaluated via extensive simulations and real experiments with a commercial IMU provided with a calibration certificate as reference data. David Tedaldi, Alberto Pretto, Emanuele Menegatti |
ICRA | 1 |