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Raaghav Radhakrishnan

dblp:301/8175 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

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 › appearance-based localization
ground texture localization
0.612022
HD Ground - A Database for Ground Texture Based Localization · ICRA 2022
Robotics › Robot navigation and mapping › localization
robot localization
0.612022
HD Ground - A Database for Ground Texture Based Localization · ICRA 2022
Robotics › Robot navigation and mapping › mobile robot navigation › route navigation
teach-and-repeat navigation
0.212022
HD Ground - A Database for Ground Texture Based Localization · ICRA 2022

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

benchmark dataset · 0.6
YearPublicationVenuePosition
2022 HD Ground - A Database for Ground Texture Based Localization
abstract
We present the HD Ground Database, a comprehensive database for ground texture based localization. It contains sequences of a variety of textures, obtained using a downward facing camera. In contrast to existing databases of ground images, the HD Ground Database is larger, has a greater variety of textures, and has a higher image resolution with less motion blur. Also, our database enables the first systematic study of how natural changes of the ground that occur over time affect localization performance, and it allows to examine a teach-and-repeat navigation scenario. We use the HD Ground Database to evaluate four state-of-the-art localization approaches for global localization, localization with the approximate pose being known, and relative localization.
Jan Fabian Schmid, Stephan F. Simon, Raaghav Radhakrishnan, Simone Frintrop, Rudolf Mester
ICRA3