论文标题
事件辅助的直接稀疏探针计
Event-aided Direct Sparse Odometry
论文作者
论文摘要
我们介绍了EDS，这是一种使用事件和帧的直接单眼视觉探空仪。我们的算法利用事件生成模型在框架之间的盲时间中跟踪相机运动。该方法制定了观察到的亮度增量的直接概率方法。使用稀疏数量的3D点预测每个像素亮度增量，并通过亮度增量误差将其与事件进行比较，以估算摄像机运动。该方法使用光度束调节恢复半密集的3D图。 EDS是使用具有直接方法的事件和帧执行6-DOF VO的第一种方法。通过设计，它克服了间接方法中外观变化的问题。我们还表明，对于目标误差性能，ED可以比基于最新框架的VOUTESS以较低的帧速率工作。这为低功率运动跟踪应用打开了大门，在这些应用程序中，框架是“按需”，我们的方法跟踪了两者之间的运动。我们向公众发布代码和数据集。
We introduce EDS, a direct monocular visual odometry using events and frames. Our algorithm leverages the event generation model to track the camera motion in the blind time between frames. The method formulates a direct probabilistic approach of observed brightness increments. Per-pixel brightness increments are predicted using a sparse number of selected 3D points and are compared to the events via the brightness increment error to estimate camera motion. The method recovers a semi-dense 3D map using photometric bundle adjustment. EDS is the first method to perform 6-DOF VO using events and frames with a direct approach. By design, it overcomes the problem of changing appearance in indirect methods. We also show that, for a target error performance, EDS can work at lower frame rates than state-of-the-art frame-based VO solutions. This opens the door to low-power motion-tracking applications where frames are sparingly triggered "on demand" and our method tracks the motion in between. We release code and datasets to the public.
