Adaptive Accurate Localization Based on Ranging for Unknown Indoor Environment

The positioning accuracy and range of ultra-wideband (UWB) are restricted by the number and placement of base stations, and we need to install base stations in advance and measure their precise locations. How to obtain high positioning accuracy without the limitation of traditional usage is a hot issue in research. To solve this problem, a novel range/visual/inertial fusion localization algorithm for unknown indoor environment has been proposed. First, we extract point and line features from images and fuse them with inertial measurement unit (IMU) measurements. Second, we arbitrarily place multiple static base stations in the environment so that the base station can estimate its relative position when visible, and at the same time, the base station can form a ranging constraint on visual inertial odometry (VIO) to correct its cumulative error. We set filters and synchronization mechanisms for UWB range measurements and fuse them with VIO in a tightly coupled manner. The proposed algorithm does not have the UWB base station position as a prior value, and the local configuration can adapt to any number of base stations. Finally, we conduct experiments in different indoor environments, and the results show that the proposed algorithm has better performance.