  Wireless signal strength based localization can enable robust localization
for robots using inexpensive sensors. For this, a location-to-signal-strength
map has to be learned for each access point in the environment. Due to the
ubiquity of Wireless networks in most environments, this can result in tens or
hundreds of maps. To reduce the dimensionality of this problem, we employ
autoencoders, which are a popular unsupervised approach for feature extraction
and data compression. In particular, we propose the use of sparse autoencoders
that learn latent spaces that preserve the relative distance between inputs.
Distance invariance between input and latent spaces allows our system to
successfully learn compact representations that allow precise data
reconstruction but also have a low impact on localization performance when
using maps from the latent space rather than the input space. We demonstrate
the feasibility of our approach by performing experiments in outdoor
environments.
