Abstract
Object pose tracking is a fundamental and essential task for robotics to perform tasks in the home and industrial settings. The most commonly used sensors to do so are RGB-D cameras, which can hit limitations in highly dynamic environments due to motion blur and frame-rate constraints. Event cameras have remarkable features such as high temporal resolution and low latency, which make them a potentially ideal vision sensors for object pose tracking at high speed. Even so, there are still only few works on 6D pose tracking with event cameras. In this work, we take advantage of the high temporal resolution and propose a method that uses both a propagation step fused with a pose correction strategy. Specifically, we use 6D object velocity obtained from event-based optical flow for pose propagation, after which, a template-based local pose correction module is utilized for pose correction. Our learning-free method has comparable performance to the state-of-the-art algorithms, and in some cases out performs them for fast-moving objects. The results indicate the potential for using event cameras in highly-dynamic scenarios where the use of deep network approaches are limited by low update rates.
Abstract (translated)
物体姿态跟踪是机器人在家庭和工业环境中执行任务的基础且必不可少的任务。最常用的传感器是RGB-D相机，但在动态强烈的环境下，这些设备可能会因运动模糊和帧率限制而受到限制。事件相机具有高时间分辨率和低延迟等显著特性，使其成为高速物体姿态跟踪的理想视觉传感器。尽管如此，目前利用事件相机进行6D姿态追踪的研究仍然很少。 在这项工作中，我们充分利用了事件相机的高时间分辨率，并提出了一种结合传播步骤与姿态修正策略的方法。具体来说，我们使用从基于事件的光流中获得的6D物体速度来执行姿态传播，在此之后，采用基于模板的局部姿态校正模块来进行姿态修正。我们的无学习方法在性能上可媲美当前最先进的算法，并且对于快速移动的对象，在某些情况下甚至超越了它们的表现。这些结果表明，事件相机在更新率较低的传统深度网络受限的应用场景中具有巨大的潜力。 通过这种方法，我们不仅展示了如何克服传统RGB-D传感器的限制，还为高速度和动态环境中的物体姿态跟踪开辟了一条新的途径。这为进一步探索事件相机在机器人任务中的应用奠定了基础，并可能开启一系列新的应用场景和技术突破。
URL
https://arxiv.org/abs/2603.08264
