  Fingerprinting-based positioning significantly improves the indoor
localization performance in non-line-of-sight-dominated areas. However, its
deployment and maintenance is cost-intensive as it needs ground-truth reference
systems for both the initial training and the adaption to environmental
changes. In contrast, channel charting (CC) works without explicit reference
information and only requires the spatial correlations of channel state
information (CSI). While CC has shown promising results in modelling the
geometry of the radio environment, a deeper insight into CC for localization
using multi-anchor large-bandwidth measurements is still pending. We contribute
a novel distance metric for time-synchronized single-input/single-output CSIs
that approaches a linear correlation to the Euclidean distance. This allows to
learn the environment's global geometry without annotations. To efficiently
optimize the global channel chart we approximate the metric with a Siamese
neural network. This enables full CC-assisted fingerprinting and positioning
only using a linear transformation from the chart to the real-world
coordinates. We compare our approach to the state-of-the-art of CC on two
different real-world data sets recorded with a 5G and UWB radio setup. Our
approach outperforms others with localization accuracies of 0.69m for the UWB
and 1.4m for the 5G setup. We show that CC-assisted fingerprinting enables
highly accurate localization and reduces (or eliminates) the need for annotated
training data.
