The Two-State Implicit Filter Recursive Estimation for Mobile Robots

This letter deals with recursive filtering for dynamic systems where an explicit process model is not easily devisable. Most Bayesian filters assume the availability of such an explicit process model, and thus may require additional assumptions or fail to properly leverage all available information. In contrast, we propose a filter that employs a purely residual-based modeling of the available information and thus achieves higher modeling flexibility. While this letter is related to the descriptor Kalman filter, it also represents a step toward batch optimization and allows the integration of further techniques, such as robust weighting for outlier rejection. We derive recursive filter equations that exhibit similar computational complexity when compared to their Kalman filter counterpart-the extended information filter. The applicability of the proposed approach is experimentally confirmed on two different real mobile robotic state estimation problems.