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J. Ni

dblp:00/4237 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › sensor calibration
lidar calibration
0.011993
Dynamic calibration and compensation of a 3D laser radar scanning system · IEEE Trans. Robotics Autom. 1993
Robotics › Robot navigation and mapping
sensor calibration
0.011993
Dynamic calibration and compensation of a 3D laser radar scanning system · IEEE Trans. Robotics Autom. 1993
Image and video processing
image restoration
0.011993
Dynamic calibration and compensation of a 3D laser radar scanning system · IEEE Trans. Robotics Autom. 1993
Audio and music processing
noise compensation
0.011993
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
YearPublicationVenuePosition
1993 Dynamic calibration and compensation of a 3D laser radar scanning system
abstract
LIDAR (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