Robot localization and mapping problem with unknown noise characteristics

Hamzah Ahmad, Toru Namerikawa
2010 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
more » ... 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.
doi:10.1109/cca.2010.5611272 dblp:conf/IEEEcca/AhmadN10 fatcat:wuvllmzdcbdqzppdov7l7gqybi