论文标题
利用事件摄像机用于快速变化轨迹的时空预测
Exploiting Event Cameras for Spatio-Temporal Prediction of Fast-Changing Trajectories
论文作者
论文摘要
本文研究了机器人技术的轨迹预测，以改善机器人与移动目标的相互作用，例如捕获弹跳球。基于回归的拟合程序无法轻易预测出意外的非线性轨迹，因此我们采用了最先进的机器学习状态，特别是基于长期术语内存（LSTM）体系结构。此外，使用事件摄像机更好地感知了快速移动的目标，该摄像机会产生由空间变化触发的异步输出，而不是像传统摄像机一样以固定的时间间隔触发。我们研究了如何适应LSTM模型以适应事件摄像机数据，特别是查看使用异步采样数据的好处。
This paper investigates trajectory prediction for robotics, to improve the interaction of robots with moving targets, such as catching a bouncing ball. Unexpected, highly-non-linear trajectories cannot easily be predicted with regression-based fitting procedures, therefore we apply state of the art machine learning, specifically based on Long-Short Term Memory (LSTM) architectures. In addition, fast moving targets are better sensed using event cameras, which produce an asynchronous output triggered by spatial change, rather than at fixed temporal intervals as with traditional cameras. We investigate how LSTM models can be adapted for event camera data, and in particular look at the benefit of using asynchronously sampled data.
