MSHF: Multi-sensor hierarchical fusion for UGV localization in unstructured environment

Multi-sensor fusion has been proven to be an efficient option for precise localization for unmanned ground vehicle (UGV) in GNSS-denied situations. This work presents a multi-sensor hierarchical fusion (MSHF) method for simultaneous localization and mapping (SLAM). Different from the existing methods, this work incorporates Odometer, Gear, and Steering wheel angle (OGS) information of a ground vehicle. In the first-level sensor fusion, the OGS data are fused with Inertial Measurement Unit (IMU) data to obtain a prior estimate of the vehicle state, where the adaptive extended Kalman filter is utilized to address the nonstationary measurement noise in the OGS data. The estimate is then fused with the one obtained by a conventional LiDAR based SLAM method in the second-level sensor fusion to provide a global optimal estimate, where the covariance intersection (CI) method is utilized for fusion of estimates with unknown correlation. The efficacy of the proposed method is demonstrated via a series of experiments and compared to the conventional algorithms using our self-generated datasets.