3 research outputs found
Dirichlet process approach for radio-based simultaneous localization and mapping
Due to 5G millimeter wave (mmWave), spatial channel parameters are becoming
highly resolvable, enabling accurate vehicle localization and mapping. We
propose a novel method of radio simultaneous localization and mapping (SLAM)
with the Dirichlet process (DP). The DP, which can estimate the number of
clusters as well as clustering, is capable of identifying the locations of
reflectors by classifying signals when such 5G signals are reflected and
received from various objects. We generate birth points using the measurements
from 5G mmWave signals received by the vehicle and classify objects by
clustering birth points generated over time. Each time we use the DP clustering
method, we can map landmarks in the environment in challenging situations where
false alarms exist in the measurements and change the cardinality of received
signals. Simulation results demonstrate the performance of the proposed scheme.
By comparing the results with the SLAM based on the Rao-Blackwellized
probability hypothesis density filter, we confirm a slight drop in SLAM
performance, but as a result, we validate that it has a significant gain in
computational complexity