| Xu Weijun（徐巍军）,Jiang Rongxin,Xie Li,Tian Xiang,Chen Yaowu.[J].高技术通讯(英文),2016,22(1):38~46 |
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| Robust SLAM using square-root cubature Kalman filter and Huber’s GM-estimator |
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| DOI：10.3772/j.issn.1006-6748.2016.01.006 |
| 中文关键词: |
| 英文关键词: square-root cubature Kalman filter, simultaneous localization and mapping (SLAM), Huber’s GM-estimator, robustness |
| 基金项目: |
| Author Name | Affiliation | | Xu Weijun（徐巍军） | | | Jiang Rongxin | | | Xie Li | | | Tian Xiang | | | Chen Yaowu | |
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| 中文摘要: |
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| 英文摘要: |
| 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 measurement noise, a robust SLAM algorithm is proposed. It is based on the square-root cubature Kalman 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 suppressed by lower Kalman gains as merging into the system. The proposed algorithm can achieve better 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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