Graph-Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS-Degraded Environment

Zheng Gong, Peilin Liu, Fei Wen, Rendong Ying, Xingwu Ji, Ruihang Miao, Wuyang Xue

Abstract: Consistent and accurate global positioning is a crucial problem for autonomous vehicles and robots. It is especially challenging in situations with a global navigation satellite system (GNSS) being intermittently degraded. We propose an adaptive fusion system, namely, GNSS/visual-inertial navigation system (GVINS), which adaptively fuses GNSS and visual-inertial odometry (VIO) to achieve consistent and accurate global positioning, even in GNSS intermittently degraded scenarios. Compared with existing methods, GVINS can provide positioning under an Earth-fixed geographic coordinate, rather than a local tangent plane (LTP) coordinate, as long as any GNSS measurement presents in the trajectory. To adaptively fuse VIO and GNSS, we first use an inertial measurement unit (IMU) preintegration-based depth uncertainty estimation method to evaluate the accuracy of VIO. Then, in the optimization backend, we perform an innovative overparameterized, 15 degree-of-freedom pose-graph fusion. An alternating minimization (AM) algorithm is used to efficiently solve this problem. Evaluation results on both public and custom-built data sets demonstrate that GVINS outperforms state-of-the-art fusion methods in both accuracy and stability in GNSS intermittently degraded environments.