Robust SLAM using square-root cubature Kalman filter and Huber’ s GM-estimator |
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引用本文: | 徐巍军,Jiang Rongxin,Xie Li,Tian Xiang,Chen Yaowu.Robust SLAM using square-root cubature Kalman filter and Huber’ s GM-estimator[J].高技术通讯(英文版),2016(1):38-46. |
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作者姓名: | 徐巍军 Jiang Rongxin Xie Li Tian Xiang Chen Yaowu |
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基金项目: | the National High Technology Research and Development Program of China(2010AA09Z104),the Fundamental Research Funds of the Zhejiang University (2014FZA5020). |
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摘 要: | Mobile robot systems performing simultaneous localization and mapping ( SLAM) are generally plagued by non-Gaussian noise.To improve both accuracy and robustness under non-Gaussian meas-urement noise, a robust SLAM algorithm is proposed.It is based on the square-root cubature Kal-man filter equipped with a Huber’ s generalized maximum likelihood estimator ( GM-estimator) .In particular, the square-root cubature rule is applied to propagate the robot state vector and covariance matrix in the time update, the measurement update and the new landmark initialization stages of the SLAM.Moreover, gain weight matrices with respect to the measurement residuals are calculated by utilizing Huber’ s technique in the measurement update step.The measurement outliers are sup-pressed by lower Kalman gains as merging into the system.The proposed algorithm can achieve bet-ter performance under the condition of non-Gaussian measurement noise in comparison with benchmark algorithms.The simulation results demonstrate the advantages of the proposed SLAM algorithm.
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关 键 词: | square-root cubature Kalman filter simultaneous localization and mapping ( SLAM) Huber’s GM-estimator robustness |
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