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Sneh Acharya

dblp:339/2750 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
0000-0003-3428-2002ORCID · reported

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

Computer networks · 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%
Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 77% Accessibility and assistive technology · 23%
Computer networks
1 paper
Internet of things and sensor networks · 100%

Topics — the 2 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot navigation and mapping › robot mapping › map management
map update
0.812024
Multimodal Smartphone based IoT Framework for Assisting People with Disabilities in SLAM based Human Robot Interaction · MobiCom 2024
Robotics › Robot navigation and mapping
SLAM
0.812024
Multimodal Smartphone based IoT Framework for Assisting People with Disabilities in SLAM based Human Robot Interaction · MobiCom 2024

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

multimodal interface · 2.3
YearPublicationVenuePosition
2024 Multimodal Smartphone based IoT Framework for Assisting People with Disabilities in SLAM based Human Robot Interaction
abstract
In this paper, a novel Multimodal Smartphone based IoT framework is proposed that interconnects human user with robotic system in performing SLAM based Human Robot Interactive tasks. Through this system, humans are added in the loop to build and update the SLAM map where they send commands to add and change locations on top of the 2D SLAM map and navigate to locations using the SLAM map when users issue a navigation command. Preliminary experiments conducted in a lab environment showed that the system can build and update SLAM maps, using user voice and text commands. Adding or changing a location in the SLAM map through user speech and text commands took an average time of 9.7 seconds while the robotic system took an average time of 40.96 seconds to complete a navigation task based on user commands using the SLAM map with an average distance error of 10.4 cm.
Harish Ram Nambiappan, Sneh Acharya, Fillia Makedon
MobiCom2