Enhancing Feature Tracking With Gyro Regularization

TL;DR

This paper introduces a novel method that integrates low-cost gyroscopes directly into feature tracking algorithms, improving robustness and performance without increasing computational complexity.

Contribution

The paper proposes a new approach to incorporate gyroscope data as a regularizer in feature tracking, enhancing accuracy especially for ambiguous or poor-quality features.

Findings

Significant performance improvements over traditional template-based tracking.

Competitive with complex state-of-the-art trackers at lower computational cost.

Gyro-based initialization offers no advantage over optical-only methods.

Abstract

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.