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Robot localization and mapping problem with unknown noise characteristics

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2 Author(s)
Hamzah Ahmad ; Div. of Electrical Engineering and Computer Science, Graduate School of Natural Science and Technology, Kanazawa University, Kakuma-Machi, 920-1192, Ishikawa, Japan ; Toru Namerikawa

In this paper, we examine the H Filter-based SLAM especially about its convergence properties. In contrast to Kalman Filter approach that considers zero mean gaussian noise, 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 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 performs better than Kalman Filter for a case of bigger robot initial uncertainties. Subsequently, this proved that Filter can provide another available estimation method for especially in SLAM.

Published in:

2010 IEEE International Conference on Control Applications

Date of Conference:

8-10 Sept. 2010