Graph Based Adaptive Fusion of GNSS and VIO Under Intermittent GNSS Degraded Environment
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.
IEEE Transactions on Instrumentation and Measurement