  Crowd-sourcing has become a promising way to build} a feature-based indoor
positioning system that has lower labour and time costs. It can make full use
of the widely deployed infrastructure as well as built-in sensors on mobile
devices. One of the key challenges is to generate the reference feature map
(RFM), a database used for localization, by {aligning crowd-sourced
{trajectories according to associations embodied in the data. In order to
facilitate the data fusion using crowd-sourced inertial sensors and radio
signals, this paper proposes an approach to adaptively mining geometric
information. This is the essential for generating spatial associations between
trajectories when employing graph-based optimization methods. The core idea is
to estimate the functional relationship to map the similarity/dissimilarity
between radio signals to the physical space based on the relative positions
obtained from inertial sensors and their associated radio signals. Namely, it
is adaptable to different modalities of data and can be implemented in a
self-supervised way. We verify the generality of the proposed approach through
comprehensive experimental analysis: i) qualitatively comparing the estimation
of geometric mapping models and the alignment of crowd-sourced trajectories;
ii) quantitatively evaluating the positioning performance. The 68\% of the
positioning error is less than 4.7 $\mathrm{m}$ using crowd-sourced RFM, which
is on a par with manually collected RFM, in a multi-storey shopping mall, which
covers more than 10, 000 $ \mathrm{m}^2 $.
