Robust SVSF-SLAM for Unmanned Vehicle in Unknown Environment

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• 作者：Fethi Demim^* ; ^{demifethi@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Abdelkrim Nemra^** ; ^{karim_nemra@yahoo.fr" class="auth_mail" title="E-mail the corresponding author} ; Kahina Louadj^*** ; ^{louadj_kahina@yahoo.fr" class="auth_mail" title="E-mail the corresponding author}
• 关键词：localization ; mapping ; feature extraction ; data association ; EKF-SVSF filters ; mobile robot
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：21
• 页码：386-394
• 全文大小：1213 K

文摘

Simultaneous localization and mapping (SLAM) is an important topic in the autonomous mobile robot research. The most popular solutions of this problem are the EKF-SLAM and the FAST-SLAM, the former requires an accurate process and observation model and suffer from the linearization problem, and the latter is not suitable for real time implementation. Therefore, a new alternative solution based on the smooth variable structure filter (SVSF-SLAM) algorithm is proposed in this paper to solve the Unmanned Ground Vehicle (UGV) SLAM problem. The SVSF filter which is formulated in a predictor-corrector format is robust face parameters uncertainties and error modeling and doesn’t require any assumption about noise characteristics. In this paper the SVSF-SLAM algorithm is implemented using the odometer and LASER data to construct a map of the environment and localize the UGV within this map. The proposed algorithm is validated and compared to the EKF-SLAM algorithm. Good results are obtained by the SVSF-SLAM comparing to the EKF-SLAM especially when the noise is colored or affected by a variable bias. Which confirm the efficiency of our approaches.