The Two-State Implicit Filter Recursive Estimation for Mobile Robots

Michael Bloesch, Michael Burri, Hannes Sommer, Roland Siegwart, Marco Hutter
2018 IEEE Robotics and Automation Letters  
This paper 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 which employs a purely residual based modeling of the available information and thus achieves higher modeling flexibility. While this work is related to the descriptor
more » ... Kalman filter, it also represents a step towards batch optimization and allows the integration of further techniques such as robust weighting for outlier rejection. We derive recursive filter equations which 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.
doi:10.1109/lra.2017.2776340 dblp:journals/ral/BloeschBSSH18 fatcat:p7olrefqxjdr7i4pt62qdzfb7u