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In-Soo Jeong

dblp:64/353 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2001
—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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › localization
robot localization
0.012001
Self-localization for Mobile Robots by Matching Two Consecutive Environmental Range Data · ICRA 2001
Robotics › Robot navigation and mapping › mobile robot perception
omnidirectional perception
0.012001
Self-localization for Mobile Robots by Matching Two Consecutive Environmental Range Data · ICRA 2001

Methods — techniques the papers use, named apart from their topics

scan matching · 0.0
YearPublicationVenuePosition
2001 Self-localization for Mobile Robots by Matching Two Consecutive Environmental Range Data
abstract
While navigating, most autonomous mobile robots view things only in front of them and, as a result, they may collide with objects moving from the side or behind. To overcome this problem, an active omni-directional range sensor system is presented, that is capable of obtaining the omni-directional range data on a navigation environment through the use of a laser conic plane and a conic mirror. Based on this system configuration, we propose a self-localization algorithm. The proposed algorithm to estimate the current position and heading angle of mobile robots utilizes the registered range data obtained at two positions; current and previous and matches the two range informations. To show the effectiveness of the proposed algorithm, a series of simulations was conducted under various navigation conditions. The results show that the proposed algorithm is very efficient in processing, and can be effectively utilized for self-localization of mobile robots in unknown environments.
In-Soo Jeong, Hyung Suck Cho
ICRA1