Factor Graph Based Incremental Smoothing in Inertial Navigation Systems
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This paper describes a new approach for information fusion in inertial navigation systems. In contrast to the commonly used filtering techniques, the proposed approach is based on a non-linear optimization for processing incoming measurements from the inertial measurement unit (IMU) and any other available sensors into a navigation solution. A factor graph formulation is introduced that allows multi-rate, asynchronous, and possibly delayed measurements to be incorporated in a natural way. This method, based on a recently developed incremental smoother, automatically determines the number of states to recompute at each step, effectively acting as an adaptive fixed-lag smoother. This yields an efficient and general framework for information fusion, providing nearly-optimal state estimates. In particular, incoming IMU measurements can be processed in real time regardless to the size of the graph. The proposed method is demonstrated in a simulated environment using IMU, GPS and stereo vision measurements and compared to the optimal solution obtained by a full non-linear batch optimization and to a conventional extended Kalman filter (EKF).