  The sensing and positioning capabilities foreseen in 6G have great potential
for technology advancements in various domains, such as future smart cities and
industrial use cases. Channel charting has emerged as a promising technology in
recent years for radio frequency-based sensing and localization. However, the
accuracy of these techniques is yet far behind the numbers envisioned in 6G. To
reduce this gap, in this paper, we propose a novel channel charting technique
capitalizing on the time of arrival measurements from surrounding Transmission
Reception Points (TRPs) along with their locations and leveraging sensor fusion
in channel charting by incorporating laser scanner data during the training
phase of our algorithm. The proposed algorithm remains self-supervised during
training and test phases, requiring no geometrical models or user position
ground truth. Simulation results validate the achievement of a sub-meter level
localization accuracy using our algorithm 90% of the time, outperforming the
state-of-the-art channel charting techniques and the traditional
triangulation-based approaches.
