Despite the promise of superior performance under challenging conditions,
event-based motion estimation remains a hard problem owing to the difficulty of
extracting and tracking stable features from event streams. In order to
robustify the estimation, it is generally believed that fusion with other
sensors is a requirement. In this work, we demonstrate reliable, purely
event-based visual odometry on planar ground vehicles by employing the
constrained non-holonomic motion model of Ackermann steering platforms. We
extend single feature n-linearities for regular frame-based cameras to the case
of quasi time-continuous event-tracks, and achieve a polynomial form via
variable degree Taylor expansions. Robust averaging over multiple event tracks
is simply achieved via histogram voting. As demonstrated on both simulated and
real data, our algorithm achieves accurate and robust estimates of the
vehicle's instantaneous rotational velocity, and thus results that are
comparable to the delta rotations obtained by frame-based sensors under normal
conditions. We furthermore significantly outperform the more traditional
alternatives in challenging illumination scenarios. The code is available at
https://github.com/gowanting/NHEVO.

基于事件的运动估计在提取和跟踪稳定特征方面仍然存在困难，因此通常认为与其他传感器的融合是必需的。在本研究中，我们利用 Ackermann 转向平台的约束非完整运动模型，实现了可靠的纯事件型视觉测距。通过将常规帧摄像机的单特征非线性扩展到准时间连续事件轨迹，并通过变量程度泰勒展开实现多项式形式，我们通过直方图投票轻松实现对多个事件轨迹的鲁棒平均值。在模拟和真实数据上的实验表明，我们的算法实现了准确而稳健的车辆瞬时旋转速度估计，从而在正常条件下与基于帧传感器获得的增量旋转相当。此外，我们在具有挑战性的照明场景中明显优于传统的替代方法。代码可在 https://github.com/gowanting/NHEVO 上获取。