  The paper proposes a multi-modal sensor fusion algorithm that fuses WiFi,
IMU, and floorplan information to infer an accurate and dense location history
in indoor environments. The algorithm uses 1) an inertial navigation algorithm
to estimate a relative motion trajectory from IMU sensor data; 2) a WiFi-based
localization API in industry to obtain positional constraints and geo-localize
the trajectory; and 3) a convolutional neural network to refine the location
history to be consistent with the floorplan.
  We have developed a data acquisition app to build a new dataset with WiFi,
IMU, and floorplan data with ground-truth positions at 4 university buildings
and 3 shopping malls. Our qualitative and quantitative evaluations demonstrate
that the proposed system is able to produce twice as accurate and a few orders
of magnitude denser location history than the current standard, while requiring
minimal additional energy consumption. We will publicly share our code, data
and models.
