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Lunfei Liang

dblp:143/0421 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2024
—ORCID · unresolved

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.

Computer networks
1 paper
Wireless sensing and localization · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Wireless sensing and localization
indoor mapping
0.812024
Demo Abstract: Range-SLAM: UWB based Realtime Indoor Location and Mapping · IPSN 2024
Wireless sensing and localization
simultaneous localization and mapping
0.812024
Demo Abstract: Range-SLAM: UWB based Realtime Indoor Location and Mapping · IPSN 2024
Wireless sensing and localization › indoor localization
ultra-wideband localization
0.812024
Demo Abstract: Range-SLAM: UWB based Realtime Indoor Location and Mapping · IPSN 2024
Wireless sensing and localization
received signal strength
0.212024
Demo Abstract: Range-SLAM: UWB based Realtime Indoor Location and Mapping · IPSN 2024

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

binary filtering · 0.8RSSI recognition · 0.8
YearPublicationVenuePosition
2024 Demo Abstract: Range-SLAM: UWB based Realtime Indoor Location and Mapping
abstract
Simultaneous localization and mapping (SLAM) systems frequently employ LiDAR and cameras as essential sensing components. However, these sensors are proved to be unreliable in environments with poor visibility or reflective surfaces. And UWB (Ultra Wide Band) sensor with a longer wavelength shows better potential to achieve perception tasks. However, since UWB sensors can only obtain distance information from the anchors, it is difficult to densely construct the geometric structure of the environment. In this paper, We propose Range-SLAM, a method based on received signal strength indicator (RSSI) recognition and binary filtering to complete the mapping task and enhance positioning based on the map, and only require UWB as external perception sensor. Real-world experiments are conducted and prove the effectiveness, real-time performance and robustness of the Range-SLAM algorithm.
Zhuozhu Jian, Junbo Tan, Lunfei Liang, Houde Liu, Xinlei Chen
IPSN4