标题： 雷达与事件相机融合用于敏捷机器人自运动估计 显示英文标题

标题： Radar and Event Camera Fusion for Agile Robot Ego-Motion Estimation

摘要： 实现敏捷机器人（例如特技飞机）的可靠自我运动估计仍然具有挑战性，因为大多数机器人传感器无法及时且清晰地响应高度动态的机器人运动，这通常会导致测量模糊、失真和延迟。 在本文中，我们提出了一种无需IMU和特征关联的框架，通过结合两种类型的外感受传感器——事件相机和毫米波雷达，以在高度动态场景中实现机器人平台的激进自我运动速度估计。 首先，我们使用瞬时原始事件和多普勒测量来直接推导旋转和线性速度。 由于不需要测量帧之间的复杂关联过程，该方法在无纹理和无结构的环境中更具鲁棒性，并且对边缘计算设备更高效。 然后，在后端，我们提出了一种连续时间状态空间模型，以融合基于时间的混合测量和事件测量，以固定滞后平滑的方式估计自我运动速度。 最后，我们在自收集的实验数据集上广泛验证了我们的速度计框架。 结果表明，我们的无需IMU和关联的自我运动估计框架可以在具有挑战性的环境中实现可靠且高效的速度输出。 源代码、示例视频和数据集可在https://github.com/ZzhYgwh/TwistEstimator获取。 显示英文摘要

摘要： Achieving reliable ego motion estimation for agile robots, e.g., aerobatic aircraft, remains challenging because most robot sensors fail to respond timely and clearly to highly dynamic robot motions, often resulting in measurement blurring, distortion, and delays. In this paper, we propose an IMU-free and feature-association-free framework to achieve aggressive ego-motion velocity estimation of a robot platform in highly dynamic scenarios by combining two types of exteroceptive sensors, an event camera and a millimeter wave radar, First, we used instantaneous raw events and Doppler measurements to derive rotational and translational velocities directly. Without a sophisticated association process between measurement frames, the proposed method is more robust in texture-less and structureless environments and is more computationally efficient for edge computing devices. Then, in the back-end, we propose a continuous-time state-space model to fuse the hybrid time-based and event-based measurements to estimate the ego-motion velocity in a fixed-lagged smoother fashion. In the end, we validate our velometer framework extensively in self-collected experiment datasets. The results indicate that our IMU-free and association-free ego motion estimation framework can achieve reliable and efficient velocity output in challenging environments. The source code, illustrative video and dataset are available at https://github.com/ZzhYgwh/TwistEstimator.