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Ryujirou Kurosaki

dblp:37/1418 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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 · 77% Legged, aerial and field robots · 23%
Network and information security
1 paper
Cyber-physical and IoT security · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › mobile robot navigation
outdoor navigation
0.112005
Development of an Autonomous Mobile Surveillance System Using a Network-based RTK-GPS · ICRA 2005
Robotics › Legged, aerial and field robots › field robotics
field robot navigation
0.012005
Development of an Autonomous Mobile Surveillance System Using a Network-based RTK-GPS · ICRA 2005

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

omnidirectional vision · 0.1laser radar · 0.1RTK-GPS · 0.1RTK GPS · 0.1
YearPublicationVenuePosition
2005 Development of an Autonomous Mobile Surveillance System Using a Network-based RTK-GPS
abstract
This paper describes an autonomous mobile surveillance system usually used in a factory premises with some high-rise buildings. This system consists of a wireless LAN network, a base station and an autonomous vehicle. The vehicle is equipped with a GPS/INS navigation system using the network-based Real-Time Kinematic GPS (RTK-GPS) with Positioning Augmentation Services (PASTM, Mitsubishi Electric Corporation 2003), an Area Laser Radar (ALR), a CCD camera called slaved camera, and an Omni-Directional Vision (ODV) sensor for surveillance and reconnaissance. The vehicle switches navigation modes according to the vehicle status. It has three guidance modes, which are “Normal”, “Road tracking”, and “Crossing recognition”. A field test result shows that the vehicle can track the planned path within 0.10[m] accuracy at straight paths and within 0.25[m] for curved paths even if RTK fixed solutions are not available. Field experiments and analyses have proved that the proposed navigation method can provide good enough guidance accuracy even under poor satellite visibility and that the panorama image database with absolute position is useful for surveillance.
Jun-ichi Meguro, Takumi Hashizume, Jun-ichi Takiguchi, Ryujirou Kurosaki
ICRA4