anchor self-localization, error-state Kalman filter, sensor fusion, simultaneous localization and mapping
Localization systems utilizing Ultra-WideBand (UWB) have been widely used in dense urban and indoor environments. A moving UWB tag can be located by ranging to fixed UWB anchors whose positions are surveyed in advance. However, manually surveying the anchors is typically a dull and time-consuming process and prone to artificial errors. In this paper, we present an accurate and easy-to-use method for UWB anchor self-localization, using the UWB ranging measurements and readings from a low-cost Inertial Measurement Unit (IMU). The locations of the anchors are automatically estimated by freely moving the tag in the environment. The method is inspired by the Simultaneous Localization And Mapping (SLAM) technique used by the robotics community. A tightly-coupled Error-State Kalman Filter (ESKF) is utilized to fuse UWB and inertial measurements, producing UWB anchor position estimates and six Degrees of Freedom (6DoF) tag pose estimates. Simulated experiments demonstrate that our proposed method enables accurate self-localization for UWB anchors and smooth tracking of the tag.
Tsinghua University Press
Qin Shi, Sihao Zhao, Xiaowei Cui et al. Anchor Self-Localization Algorithm Based on UWB Ranging and Inertial Measurements. Tsinghua Science and Technology 2019, 24(06): 728-737.