  Data-driven based method for navigation and positioning has absorbed
attention in recent years and it outperforms all its competitor methods in
terms of accuracy and efficiency. This paper introduces a new architecture
called IMUNet which is accurate and efficient for position estimation on edge
device implementation receiving a sequence of raw IMU measurements. The
architecture has been compared with one dimension version of the
state-of-the-art CNN networks that have been introduced recently for edge
device implementation in terms of accuracy and efficiency. Moreover, a new
method for collecting a dataset using IMU sensors on cell phones and Google
ARCore API has been proposed and a publicly available dataset has been
recorded. A comprehensive evaluation using four different datasets as well as
the proposed dataset and real device implementation has been done to prove the
performance of the architecture. All the code in both Pytorch and Tensorflow
framework as well as the Android application code have been shared to improve
further research.
