  On-device localization and tracking are increasingly crucial for various
applications. Along with a rapidly growing amount of location data, machine
learning (ML) techniques are becoming widely adopted. A key reason is that ML
inference is significantly more energy-efficient than GPS query at comparable
accuracy, and GPS signals can become extremely unreliable for specific
scenarios. To this end, several techniques such as deep neural networks have
been proposed. However, during training, almost none of them incorporate the
known structural information such as floor plan, which can be especially useful
in indoor or other structured environments. In this paper, we argue that the
state-of-the-art-systems are significantly worse in terms of accuracy because
they are incapable of utilizing these essential structural information. The
problem is incredibly hard because the structural properties are not explicitly
available, making most structural learning approaches inapplicable. Given that
both input and output space potentially contain rich structures, we study our
method through the intuitions from manifold-projection. Whereas existing
manifold based learning methods actively utilized neighborhood information,
such as Euclidean distances, our approach performs Neighbor Oblivious Learning
(NObLe). We demonstrate our approach's effectiveness on two orthogonal
applications, including WiFi-based fingerprint localization and inertial
measurement unit(IMU) based device tracking, and show that it gives significant
improvement over state-of-art prediction accuracy.
