Event-based cameras are new type vision sensors whose pixels work independently and respond asynchronously to brightness change with microsecond resolution, instead of providing standard 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 for stereo event-based cameras based on Error-State Kalman Filter (ESKF). The visual module updates the pose relies on the edge alignment of a semi-dense 3D map to a 2D image, and the IMU module updates pose by median integral. We evaluate our method on public datasets with general 6-DoF motion and compare the results against ground truth. We show that our proposed pipeline provides improved accuracy over the result of the state-of-the-art visual odometry for stereo event-based cameras, while running in real-time on a standard CPU (low-resolution cameras). To the best of our knowledge, this is the first published visual-inertial odometry for stereo event-based 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等特性，为机器人在某些挑战性场景中的应用提供了可能性。我们提出了一种基于误差状态卡尔曼滤波的立体事件相机视觉-惯性里程计。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位姿，而惯性测量单元模块则通过中值积分更新位姿。我们在包含一般六自由度运动的公开数据集上评估了该方法，并将结果与真实值进行了对比。结果表明，我们提出的流程相较于当前最先进的立体事件相机视觉里程计方法，在精度上有所提升，同时能在标准CPU（低分辨率相机）上实时运行。据我们所知，这是首个针对立体事件相机的视觉-惯性里程计公开成果。