Demo Data from SIGIR 2026 / reviewers in the wild / expert
J. Ni
dblp:00/4237
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1993
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 · 100% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 50% Audio and music processing · 50% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping › sensor calibration
lidar calibration |
0.0 | 1 | 1993 | Dynamic calibration and compensation of a 3D laser radar scanning system · IEEE Trans. Robotics Autom. 1993 |
Robotics › Robot navigation and mapping
sensor calibration |
0.0 | 1 | 1993 | Dynamic calibration and compensation of a 3D laser radar scanning system · IEEE Trans. Robotics Autom. 1993 |
Image and video processing
image restoration |
0.0 | 1 | 1993 | Dynamic calibration and compensation of a 3D laser radar scanning system · IEEE Trans. Robotics Autom. 1993 |
Audio and music processing
noise compensation |
0.0 | 1 | 1993 | Dynamic calibration and compensation of a 3D laser radar scanning system · IEEE Trans. Robotics Autom. 1993 |
Methods — techniques the papers use, named apart from their topics
polynomial calibration · 0.0autoregressive moving average model · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1993 | Dynamic calibration and compensation of a 3D laser radar scanning systemabstractLIDAR (laser radar) is used to measure three-dimensional (3-D) object positions. It produces a range and an intensity image of the measured object and relies on the range image to determine the 3-D positions of the object. Because the range image is frequently corrupted with noise, a dynamic method to improve the LIDAR accuracy, based on a polynomial calibration model and an autoregressive moving-average calibration model, has been established. Experimental results show that the measurement errors of the LIDAR system have been reduced from 163 counts to 18 counts after compensation using the polynomial calibration model and that the errors have been further reduced to 11 counts with the ARMA model.> Y. D. Chen, J. Ni |
IEEE Trans. Robotics Autom. | 2 |