Event cameras trigger events asynchronously and independently upon a sufficient change of the logarithmic brightness level. The neuromorphic sensor has several advantages over standard cameras including low latency, absence of motion blur, and high dynamic range. Event cameras are particularly well suited to sense motion dynamics in agile scenarios. We propose the continuous event-line constraint, which relies on a constant-velocity motion assumption as well as trifocal tensor geometry in order to express a relationship between line observations given by event clusters as well as first-order camera dynamics. Our core result is a closed-form solver for up-to-scale linear camera velocity {with known angular velocity}. Nonlinear optimization is adopted to improve the performance of the algorithm. The feasibility of the approach is demonstrated through a careful analysis on both simulated and real data.
翻译:事件摄像头在对数亮度发生足够变化后,不同步和独立地触发事件。 神经形态传感器比标准相机具有若干优势, 包括低延迟度、 没有运动模糊度和高动态范围。 事件摄像头特别适合在灵活情景中感知运动动态。 我们提议持续事件线限制, 依靠恒定速度动作假设以及三维焦压强几何法来表达事件群和第一阶摄像动态的线观测之间的关系。 我们的核心结果是一个用于上至上层线性照相机速度的封闭式解析器( 已知角速度 ) 。 采用非线性优化来改进算法的性能。 方法的可行性通过对模拟数据和真实数据进行仔细分析来证明。