Direct visual-inertial odometry and mapping for unmanned vehicle

Abstract

We present a direct visual-inertial system that can track camera motions and map the environment. This method aligns input images directly based on the intensity of pixels and minimizes the photometric error, instead of using key features detected in the images. IMU measurements provide additional constraints to suppress the scale drift induced by the visual odometry. The depth information for each pixel can be computed either from the inverse depth estimation or from stereo images. Experiments using an existing dataset shows that the performance of our method is comparable to that of a latest reported method.