Enhancing Feature Tracking With Gyro Regularization
This work addresses feature tracking for applications like robotics or AR/VR by providing a more efficient and effective method, though it is incremental as it builds on existing gyroscope integration techniques.
The paper tackled the problem of improving feature tracking by integrating gyroscope data to regularize the tracking energy function, resulting in significant performance gains over conventional methods and competitive results with state-of-the-art trackers at much lower computational cost.
We present a deeply integrated method of exploiting low-cost gyroscopes to improve general purpose feature tracking. Most previous methods use gyroscopes to initialize and bound the search for features. In contrast, we use them to regularize the tracking energy function so that they can directly assist in the tracking of ambiguous and poor-quality features. We demonstrate that our simple technique offers significant improvements in performance over conventional template-based tracking methods, and is in fact competitive with more complex and computationally expensive state-of-the-art trackers, but at a fraction of the computational cost. Additionally, we show that the practice of initializing template-based feature trackers like KLT (Kanade-Lucas-Tomasi) using gyro-predicted optical flow offers no advantage over using a careful optical-only initialization method, suggesting that some deeper level of integration, like the method we propose, is needed in order to realize a genuine improvement in tracking performance from these inertial sensors.