Real-Time Pose Estimation for Event Cameras with Stacked Spatial LSTM Networks

We present a new method to estimate the 6DOF pose of the event camera solely based on the event stream. Our method first creates the event image from a list of events that occurs in a very short time interval, then a Stacked Spatial LSTM Network (SP-LSTM) is used to learn and estimate the camera pose. Our SP-LSTM comprises a CNN to learn deep features from the event images and a stack of LSTM to learn spatial dependencies in the image features space. We show that the spatial dependency plays an important role in the pose estimation task and the SP-LSTM can effectively learn that information. The experimental results on the public dataset show that our approach outperforms recent methods by a substantial margin. Overall, our proposed method reduces about 6 times the position error and 3 times the orientation error over the state of the art. The source code and trained models will be released.