A useful application of event sensing is visual odometry, especially in settings that require high-temporal resolution. The state-of-the-art method of contrast maximisation recovers the motion from a batch of events by maximising the contrast of the image of warped events. However, the cost scales with image resolution and the temporal resolution can be limited by the need for large batch sizes to yield sufficient structure in the contrast image. In this work, we propose spatiotemporal registration as a compelling technique for event-based rotational motion estimation. We theoretcally justify the approach and establish its fundamental and practical advantages over contrast maximisation. In particular, spatiotemporal registration also produces feature tracks as a by-product, which directly supports an efficient visual odometry pipeline with graph-based optimisation for motion averaging. The simplicity of our visual odometry pipeline allows it to process more than 1 M events/second. We also contribute a new event dataset for visual odometry, where motion sequences with large velocity variations were acquired using a high-precision robot arm.
翻译:事件感测的有用应用是视觉odology, 特别是在需要高时分辨率的环境下。 最先进的对比最大化方法通过对扭曲事件图像的对比度最大化,从一系列事件中恢复了运动。 但是,图像分辨率和时间分辨率的成本尺度可能因大型批量尺寸需要产生足够的对比图像结构而受到限制。 在这项工作中,我们提议在时空间进行空间登记,作为根据事件进行旋转运动估计的令人信服的技术。 我们从理论上证明这种方法合理,并确立其相对于对比最大化的基本和实际优势。 特别是, 空间时空登记还产生副产品功能轨迹,直接支持高效的视觉odorography管道,以基于图形的优化为平均运动。 我们的视觉odographic管道的简单化使得它能够处理超过1M事件/秒。 我们还为视觉odorime测量提供了一个新的事件数据集, 在那里,使用高精确度的机器人获得了具有大速度变化的动作序列。