DeepCompass: AI-driven Location-Orientation Synchronization for Navigating Platforms
This addresses a specific issue for users of navigating platforms, such as drivers, by providing an incremental improvement over existing sensor-based methods.
The paper tackles the problem of asynchronous location-orientation identification in navigating platforms, which causes incorrect direction estimates at startup, by proposing DeepCompass to synchronize orientation using street-view and user-view images without additional hardware, achieving results that are not susceptible to external interference like magnetometer-based methods.
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.