Improved monocular visual inertial odometer based on VI-DSO

Aiming at the problems that the visual odometry of the pure direct method lacks scale information, it is easy to fall into the local optimum when optimizing the pose, and the convergence speed is slow when monocular vision is initialized, an improved direct method monocular visual inertial odometry scheme is proposed. Based on the direct method visual inertial odometer VI-DSO with better effect at present, a modification scheme is proposed. In the initialization, the VI-DSO scheme ignores the initialization of the IMU, and uses the back-end unified optimization method to estimate the IMU bias, which leads to the slow convergence of the scale and the large cumulative error. The fast estimation of the IMU bias and the MAP of the scale accelerates the speed of the scale convergence during the initialization, and also provides a more accurate initial data for the back-end optimization to reduce the cumulative error. In the depth estimation, the depth filtering scheme is improved. Referring to the SVO filtering method, the Gaussian-uniform filter is used to estimate the probability of mismatching, eliminate the wrong depth estimation, and fuse the correct depth data to improve the positioning accuracy. In the process of marginalization, the marginalization strategy of the VI-DSO scheme is improved, and the judgment of the current motion state is increased. The frames that need to be marginalized are selected according to the motion state to ensure sufficient parallax in the sliding window. The test results in EuRoc dataset show that the improved scheme improves the initialization speed by 33 % and the average positioning accuracy by 34.5 %.