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Sezal Jain

dblp:135/8245 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—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
3D vision · 65% Robot navigation and mapping · 22% Legged, aerial and field robots · 13%

Topics — the 6 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › depth estimation › multi-task depth estimation
depth and pose estimation
0.212013
Infrastructure-free shipdeck tracking for autonomous landing · ICRA 2013
Computer vision › 3D vision › pose estimation › depth-based pose estimation
LiDAR-based pose estimation
0.212013
Infrastructure-free shipdeck tracking for autonomous landing · ICRA 2013
Robotics › Robot navigation and mapping
localization
0.212013
Infrastructure-free shipdeck tracking for autonomous landing · ICRA 2013
Computer vision › 3D vision › camera pose estimation
relative pose estimation
0.212013
Infrastructure-free shipdeck tracking for autonomous landing · ICRA 2013
Robotics › Legged, aerial and field robots
aerial robots
0.012013
Infrastructure-free shipdeck tracking for autonomous landing · ICRA 2013
Robotics › Legged, aerial and field robots › aerial robots
autonomous landing
0.012013
Infrastructure-free shipdeck tracking for autonomous landing · ICRA 2013

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

camera tracking · 0.2LiDAR · 0.2
YearPublicationVenuePosition
2013 Infrastructure-free shipdeck tracking for autonomous landing
abstract
Shipdeck landing is one of the most challenging tasks for a rotorcraft. Current autonomous rotorcraft use shipdeck mounted transponders to measure the relative pose of the vehicle to the landing pad. This tracking system is not only expensive but renders an unequipped ship unlandable. We address the challenge of tracking a shipdeck without additional infrastructure on the deck. We present two methods based on video and lidar that are able to track the shipdeck starting at a considerable distance from the ship. This redundant sensor design enables us to have two independent tracking systems. We show the results of the tracking algorithms in three different environments - field testing results on actual helicopter flights, in simulation with a moving shipdeck for lidar based tracking and in laboratory using an occluded, and, moving scaled model of a landing deck for camera based tracking. The complimentary modalities allow shipdeck tracking under varying conditions.
Sankalp Arora, Sezal Jain, Sebastian A. Scherer, Stephen Nuske, Lyle Chamberlain, Sanjiv Singh
ICRA2