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Amy Sniffen

dblp:282/6257 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2022
—ORCID · none

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

Computer networks · 2 · 2 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.

Computer networks
2 papers
Wireless sensing and localization · 88% Vehicular, aerial and satellite networks · 12%
Artificial intelligence
1 paper
Robot navigation and mapping · 100%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping
localization
0.612022
Sunflower: locating underwater robots from the air: video · MobiSys 2022
Robotics › Robot navigation and mapping › localization › GPS-denied localization
underwater localization
0.612022
Sunflower: locating underwater robots from the air: video · MobiSys 2022
Wireless sensing and localization
optical sensing
0.612022
Sunflower: locating underwater robots from the air · MobiSys 2022
Wireless sensing and localization › localization algorithms
underwater localization
0.612022
Sunflower: locating underwater robots from the air · MobiSys 2022
Wireless sensing and localization
aerial localization
0.212022
Sunflower: locating underwater robots from the air: video · MobiSys 2022

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

optical fiber sensing · 0.6backscatter communication · 0.6
YearPublicationVenuePosition
2022 Sunflower: locating underwater robots from the air
abstract
Locating underwater robots is fundamental for enabling important underwater applications. The current mainstream method requires a physical infrastructure with relays on the water surface, which is largely ad-hoc, introduces a significant logistical overhead, and entails limited scalability. Our work, Sunflower, presents the first demonstration of wireless, 3D localization across the air-water interface - eliminating the need for additional infrastructure on the water surface. Specifically, we propose a laser-based sensing system to enable aerial drones to directly locate underwater robots. The Sunflower system consists of a queen and a worker component on a drone and each tracked underwater robot, respectively. To achieve robust sensing, key system elements include (1) a pinhole-based sensing mechanism to address the sensing skew at air-water boundary and determine the incident angle on the worker, (2) a novel optical-fiber sensing ring to sense weak retroreflected light, (3) a laser-optimized backscatter communication design that exploits laser polarization to maximize retroreflected energy, and (4) the necessary models and algorithms for underwater sensing. Real-world experiments demonstrate that our Sunflower system achieves average localization error of 9.7 cm with ranges up to 3.8 m and is robust against ambient light interference and wave conditions.
Charles J. Carver, Qijia Shao, Samuel Lensgraf, Amy Sniffen, Maxine Perroni-Scharf, Hunter Gallant, Alberto Quattrini Li
MobiSys4
2022 Sunflower: locating underwater robots from the air: video
Charles J. Carver, Qijia Shao, Samuel Lensgraf, Amy Sniffen, Maxine Perroni-Scharf, Hunter Gallant, Alberto Quattrini Li
MobiSys4