Loosely-Coupled Camera-IMU-OGS Fusion Localization

In this paper, a hierarchical multi-sensor fusion approach is presented for localization in unmanned ground vehicle (UGV) applications where global navigation satellite system (GNSS) signals are not available. The proposed method incorporates Odometer, Gear, and Steering wheel angle (OGS) information from the UGV. At the top layer, an optimum data fusion approach based on the linear minimum variance principle and using an adaptive weighting coefficient matrix is presented for fusing each local state estimation, and at the bottom layer, adaptive extended Kalman filter (AEKF) operates as a local filter, integrating IMU and OGS data with camera to generate the global optimal estimate. The proposed methodology avoids applying upper constraints on the covariance for estimate to remove the connection between local states. We validate the performance of our system using truly collected data.