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、无运动模糊及高动态范围（HDR）等特性，为机器人应对某些具有挑战性的场景提供了可能。我们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，IMU模块则采用中点法更新位姿。我们在具有常规六自由度运动的自然场景公开数据集上评估所提方法，并将结果与地面真值进行对比。实验表明，所提方案在标准CPU上实时运行时，相较于现有最优的立体事件相机视觉里程计方法具有更高的精度。据我们所知，这是首个公开发表的面向立体事件相机的视觉-惯性里程计算法。