  Precise indoor localization is an increasingly demanding requirement for
various emerging applications, like Virtual/Augmented reality and personalized
advertising. Current indoor environments are equipped with pluralities of WiFi
access points (APs), whose deployment is expected to be massive in the future
enabling highly precise localization approaches. Though the conventional
model-based localization schemes have achieved sub-meter level accuracy by
fusing multiple channel state information (CSI) observations, the corresponding
computational overhead is usually significant, especially in the current
multiple-input multiple-output orthogonal frequency division multiplexing
(MIMO-OFDM) systems. In order to address this issue, model-free localization
techniques using deep learning frameworks have been lately proposed, where
mainly classification methods were applied. In this paper, instead of
classification based mechanism, we propose a logistic regression based scheme
with the deep learning framework, combined with Cram\'er-Rao lower bound (CRLB)
assisted robust training, which achieves more robust sub-meter level accuracy
(0.97m median distance error) in the standard laboratory environment and
maintains reasonable online prediction overhead under the single WiFi AP
settings.
