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Bunji Shigematsu

dblp:51/290 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2003
—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 · 100%

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

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
localization
0.012003
Outdoor navigation of a mobile robot between buildings based on DGPS and odometry data fusion · ICRA 2003
Robotics › Robot navigation and mapping › mobile robot navigation
outdoor navigation
0.012003
Outdoor navigation of a mobile robot between buildings based on DGPS and odometry data fusion · ICRA 2003
Robotics › Robot navigation and mapping › mobile robot navigation
map-based navigation
0.012003
Outdoor navigation of a mobile robot between buildings based on DGPS and odometry data fusion · ICRA 2003

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

multipath error rejection · 0.0extended kalman filter · 0.0
YearPublicationVenuePosition
2003 Outdoor navigation of a mobile robot between buildings based on DGPS and odometry data fusion
abstract
The authors aim at map based outdoor navigation of a mobile robot. In navigation, robot position is fundamentally obtained by odometry. However, the position is misaligned as the robot moves because odometry has cumulative error. DGPS measurement data may cancel its position error. The framework of EKF is used for the modification and the fusion between odometry and DGPS measurement data. The DGPS measurement data, however, could have large error because of multipath near buildings. In this paper, the authors propose a method which eliminates erroneous DGPS measurement data when odometry robot position is fused, and confirm the validity of this approach.
Kazunori Ohno, Takashi Tsubouchi, Bunji Shigematsu, Shoichi Maeyama, Shin'ichi Yuta
ICRA3