Demo Data from EDBT 2026 / reviewers in the wild / expert
R. M. Taylor
dblp:65/737
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1996
—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 · 39% 3D vision · 30% Representation and self-supervised learning · 30% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Representation and self-supervised learning › representation learning
feature extraction |
0.0 | 1 | 1996 | Range finding and feature extraction by segmentation of images for mobile robot navigation · ICRA 1996 |
Robotics › Robot navigation and mapping › mobile robot perception
laser range sensing |
0.0 | 1 | 1996 | Range finding and feature extraction by segmentation of images for mobile robot navigation · ICRA 1996 |
Computer vision › 3D vision
range sensing |
0.0 | 1 | 1996 | Range finding and feature extraction by segmentation of images for mobile robot navigation · ICRA 1996 |
Robotics › Robot navigation and mapping
mobile robot navigation |
0.0 | 1 | 1996 | Range finding and feature extraction by segmentation of images for mobile robot navigation · ICRA 1996 |
Methods — techniques the papers use, named apart from their topics
recursive line fitting · 0.0outlier rejection · 0.0bookstein algorithm · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1996 | Range finding and feature extraction by segmentation of images for mobile robot navigationabstractThis paper describes techniques to identify various features in a scene using a laser range finding sensor, scanning horizontally in one dimension. The authors describe the optical and electronic properties of the sensor, together with the distributed processing hardware. The authors present a method to recognise and parameterise straight lines and ellipses occurring together in ID range images. In particular, the authors: (1) Present a line fitting system which recursively fits points to line segments, given range and bearing information, and correctly accounts for the sensors error sources. This system also passes on unfitted points to the ellipse fitter. (2) Extend Rothwell and Zisserman's improvements to the Bookstein algorithm to determine ellipse parameters from few observed points which lie over a small angular excursion of the ellipse, for example a pipe in an industrial environment. (3) Discuss the problems of outliers, and develop methods of recognising and rejecting them, including the use of intensity as well as range data. The authors show solutions to these problems on real data, and finally discuss further methods they might use to improve the ellipse fitting. R. M. Taylor, Penny Probert Smith |
ICRA | 1 |