A lifted semi-direct monocular visual odometry

In this paper, we proposed a practical and efficient algorithm based on conventional semi-direct monocular visual odometry (SVO) algorithm, which mainly aims at the future application of the Simultaneous Localization and Mapping (SLAM) for embedded or mobile platforms such as robots and wearable devices. By applying the velocity momentum during the initial pose estimation, we present a novel algorithm for obtaining the initial pose, which is closer to the true value and more effective to solving the limitation of non-convergence in most existing approaches. A sparse image alignment module is also proposed to rectify the pose offset occurred at the corner, by elaborately resetting the relative pose at the location with large photometric error. The proposed lifted semi-direct monocular visual odometry has been extensively evaluated on benchmark dataset. The experimental result demonstrates that our method can explicitly generate the accurate initial poses without reducing the speed.