Asynchronous, Photometric Feature Tracking using Events and Frames
This work addresses the problem of low-latency feature tracking for applications like robotics and computer vision, offering a principled method that improves accuracy and track length over existing heuristic approaches.
The paper tackles the challenge of tracking visual features with low-latency by combining event cameras and standard cameras, resulting in feature tracks that are more accurate (subpixel accuracy) and longer than state-of-the-art methods across various scenes.
We present a method that leverages the complementarity of event cameras and standard cameras to track visual features with low-latency. Event cameras are novel sensors that output pixel-level brightness changes, called "events". They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the same scene pattern can produce different events depending on the motion direction, establishing event correspondences across time is challenging. By contrast, standard cameras provide intensity measurements (frames) that do not depend on motion direction. Our method extracts features on frames and subsequently tracks them asynchronously using events, thereby exploiting the best of both types of data: the frames provide a photometric representation that does not depend on motion direction and the events provide low-latency updates. In contrast to previous works, which are based on heuristics, this is the first principled method that uses raw intensity measurements directly, based on a generative event model within a maximum-likelihood framework. As a result, our method produces feature tracks that are both more accurate (subpixel accuracy) and longer than the state of the art, across a wide variety of scenes.