A nonlinear optimization-based monocular dense mapping system of visual-inertial odometry

Abstract Nowadays, 3D reconstruction mainly uses depth cameras. For the disadvantages that depth cameras can not get depth value outdoor and requires careful scanning to obtain accurate reconstruction effects, we propose a monocular dense mapping system. Since vision-only methods and IMU sensors are both easy to drift, we optimize the propagation strategies during the IMU preintegration. We marginalize the frames in the sliding window to bound the computational complexity. The back-end minimizes the marginalized IMU measurement residuals and visual residuals together. The four-level quadtree is applied to estimate the depth and reconstruct the map. We compare our method with five advanced odometry systems and compare the developed method with and without the IMU sensor on public EuRoC datasets. The results show that the method which involves IMU performs better than the vision-only case in general. The reconstruction result is accurate and clear.