  Fingerprint-based localization improves the positioning performance in
challenging, non-line-of-sight (NLoS) dominated indoor environments. However,
fingerprinting models require an expensive life-cycle management including
recording and labeling of radio signals for the initial training and regularly
at environmental changes. Alternatively, channel-charting avoids this labeling
effort as it implicitly associates relative coordinates to the recorded radio
signals. Then, with reference real-world coordinates (positions) we can use
such charts for positioning tasks. However, current channel-charting approaches
lag behind fingerprinting in their positioning accuracy and still require
reference samples for localization, regular data recording and labeling to keep
the models up to date. Hence, we propose a novel framework that does not
require reference positions. We only require information from velocity
information, e.g., from pedestrian dead reckoning or odometry to model the
channel charts, and topological map information, e.g., a building floor plan,
to transform the channel charts into real coordinates. We evaluate our approach
on two different real-world datasets using 5G and distributed
single-input/multiple-output system (SIMO) radio systems. Our experiments show
that even with noisy velocity estimates and coarse map information, we achieve
similar position accuracies
