Event-based cameras are new type vision sensors whose pixels work independently and respond asynchronously to brightness change with microsecond resolution, instead of provide stand-ard intensity frames. Compared with traditional cameras, event-based cameras have low latency, no motion blur, and high dynamic range (HDR), which provide possibilities for robots to deal with some challenging scenes. We propose a visual-inertial odometry method for stereo event-cameras based on Kalman filtering. The visual module updates the camera pose relies on the edge alignment of a semi-dense 3D map to a 2D image, and the IMU module updates pose by midpoint method. We evaluate our method on public datasets in natural scenes with general 6-DoF motion and compare the results against ground truth. We show that the proposed pipeline provides improved accuracy over the result of a state-of-the-art visual odometry method for stereo event-cameras, while running in real-time on a standard CPU. To the best of our knowledge, this is the first published visual-inertial odometry algorithm for stereo event-cameras.

翻译：事件相机是一种新型视觉传感器，其像素独立工作并以微秒级分辨率异步响应亮度变化，而非提供标准强度帧。与传统相机相比，事件相机具有低延迟、无运动模糊和高动态范围（HDR）等优势，为机器人应对某些挑战性场景提供了可能。本文提出一种基于卡尔曼滤波的立体事件相机视觉-惯性里程计方法。视觉模块通过将半稠密三维地图与二维图像进行边缘对齐来更新相机位姿，惯性测量单元（IMU）模块则采用中点法进行位姿更新。我们在具有一般六自由度运动的自然场景公开数据集上评估了该方法，并将结果与地面真值进行对比。结果表明，所提出的流程相较于现有最先进的立体事件相机视觉里程计方法，在精度上获得提升，同时能在标准CPU上实时运行。据我们所知，这是首个面向立体事件相机的视觉-惯性里程计算法。