Robot localization and mapping problem with unknown noise characteristics
2010 IEEE International Conference on Control Applications
In this paper, we examine the H ∞ filter-based SLAM especially about its convergence properties. In contrast to Kalman filter approach that considers gaussian noise with zero mean, H ∞ filter is more robust and may provide sufficient solutions for SLAM in an environment with unknown statistical behavior. Due to this advantage, H ∞ filter is proposed in this paper to efficiently estimate the robot and landmarks location under worst case situations. H ∞ filter requires the designer to
... gner to appropriately choose the noise's covariance with respect to γ to obtain a desired outcome. We show some of the conditions to be satisfy in order to achieve better estimation results than Kalman filter. From the experimental results, H ∞ filter is perform better than Kalman filter for a case of bigger robot initial uncertainties. These subsequently may provide another available estimation method with the capability to ensure and improve estimation for the robotic mapping problem, especially in SLAM.