DeepCompass: AI-driven Location-Orientation Synchronization for
  Navigating Platforms
Jihun Lee SP Choi Bumsoo Kang Hyekyoung Seok Hyoungseok Ahn Sanghee Jung
In current navigating platforms, the user's orientation is typically
estimated based on the difference between two consecutive locations. In other
words, the orientation cannot be identified until the second location is taken.
This asynchronous location-orientation identification often leads to our
real-life question: Why does my navigator tell the wrong direction of my car at
the beginning? We propose DeepCompass to identify the user's orientation by
bridging the gap between the street-view and the user-view images. First, we
explore suitable model architectures and design corresponding input
configuration. Second, we demonstrate artificial transformation techniques
(e.g., style transfer and road segmentation) to minimize the disparity between
the street-view and the user's real-time experience. We evaluate DeepCompass
with extensive evaluation in various driving conditions. DeepCompass does not
require additional hardware and is also not susceptible to external
interference, in contrast to magnetometer-based navigator. This highlights the
potential of DeepCompass as an add-on to existing sensor-based orientation
detection methods.