Low drift visual inertial odometry with UWB aided for indoor localization

Visual inertial odometry (VIO) would have an estimation drift problem in the process of long trajectory for indoor localization, especially in the absence of loop detection or in unknown complex scenes. To solve this problem, a low drift visual inertial odometry with ultra-wideband (UWB) aided for indoor localization was proposed. Firstly, a single UWB anchor was dropped in an unknown position, and a cost function was formed by the position information output by VIO and the UWB ranging information to obtain the position of the anchor. Then, the single anchor position and the UWB ranging constraints were added to the tightly coupled visual inertial fusion algorithm framework, thereby improving the robustness of motion tracking and reducing the drift of the odometry. Finally, the effectiveness of the proposed method was verified in the actual indoor environment, and the experiment results demonstrated that, compared with state-of-the-art localization methods, the positioning accuracy and robustness were improved significantly.