Graduate School and Research Center in Digital Sciences

Team channel-SLAM: A cooperative mapping approach to vehicle localization

Chu, Xinghe; Lu, Zhaoming; Wang, Luhan; Wen, Xiangming; Gesbert, David

Submitted on ArXiv, 17 April 2020

Vehicle positioning is considered a key element in autonomous driving systems. While conventional positioning requires the use of GPS and/or beacon signals from network infrastructure for triangulation, they are sensitive to multipath and signal obstruction. However, recent proposals like the Channel-SLAM method showed it was possible in principle to in fact leverage multi-path to improve positioning of a single vehicle. In this paper, we derive a cooperative Channel-SLAM framework, which is referred as Team Channel-SLAM. Different from the previous work, Team Channel-SLAM not only exploits the stationary nature of reflecting objects around the receiver to characterize the location of a single vehicle through multi-path signals, but also capitalizes on the multi-vehicle aspects of road traffic to further improve positioning. Specifically, Team ChannelSLAM exploits the correlation between reflectors around multiple neighboring vehicles to achieve high precision multiple vehicle positioning. Our method uses affinity propagation clustering and cooperative particle filter. The new framework is shown to give substantial improvement over the single vehicle positioning situation.

Arxiv Bibtex

Title:Team channel-SLAM: A cooperative mapping approach to vehicle localization
Keywords:vehicular localization, SLAM, positioning and tracking techniques, radio based localization, 5G
Department:Communication systems
Eurecom ref:6239
Copyright: © EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Submitted on ArXiv, 17 April 2020 and is available at :
Bibtex: @inproceedings{EURECOM+6239, year = {2020}, title = {{T}eam channel-{SLAM}: {A} cooperative mapping approach to vehicle localization}, author = {{C}hu, {X}inghe and {L}u, {Z}haoming and {W}ang, {L}uhan and {W}en, {X}iangming and {G}esbert, {D}avid}, booktitle = {{S}ubmitted on {A}r{X}iv, 17 {A}pril 2020}, address = {}, month = {04}, url = {} }
See also: