无需额外传感器，仅用事件相机即可同时估计旋转与平移运动。
Full-DoF Egomotion Estimation for Event Cameras Using Geometric Solvers
- 利用线段事件流构建几何关系，统一求解全自由度运动
- 在合成与真实数据上实现角速度和线速度的精确恢复
- 适合低延迟运动估计场景，如机器人导航与自动驾驶
对于事件相机，现有的稀疏几何求解器通常假设旋转位移已知（如由IMU提供），因此只能恢复平动参数。完全通过稀疏几何求解器恢复包含旋转与平动在内的全自由度运动参数是一项更具挑战性的任务，尚未被充分研究。本文提出若干求解器，在统一框架内估计旋转与平动速度。方法利用由线段诱导的事件流流形，基于线段的关联关系或法向量的新共面关系进行建模。实验表明，该方法无需额外传感器测量或运动先验，即可同时恢复角速度与线速度。为实现高效优化，采用带一阶旋转近似的Adam优化框架进行快速初始化。在合成与真实数据上的实验验证了方法的有效性。代码已开源：https://github.com/jizhaox/relpose-event。
原文摘要 · Abstract (English)
For event cameras, current sparse geometric solvers for egomotion estimation assume that the rotational displacements are known, such as those provided by an IMU. Thus, they can only recover the translational motion parameters. Recovering full-DoF motion parameters using a sparse geometric solver is a more challenging task, and has not yet been investigated. In this paper, we propose several solvers to estimate both rotational and translational velocities within a unified framework. Our method leverages event manifolds induced by line segments. The problem formulations are based on either an incidence relation for lines or a novel coplanarity relation for normal vectors. We demonstrate the possibility of recovering full-DoF egomotion parameters for both angular and linear velocities without requiring extra sensor measurements or motion priors. To achieve efficient optimization, we exploit the Adam framework with a first-order approximation of rotations for quick initialization. Experiments on both synthetic and real-world data demonstrate the effectiveness of our method. The code is available at https://github.com/jizhaox/relpose-event.
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