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OALib Journal期刊
ISSN: 2333-9721
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Novel FastSLAM algorithm based on square root unscented Kalman filter
一种基于SR-UKF的FastSLAM算法

Keywords: simultaneous localization and mapping(SLAM),square root unscented Kalman filter(SR-UKF),fast simulta-neous location and mapping(FastSLAM),extended Kalman filter(EKF)
同时定位与地图创建
,基于平方根的无迹卡尔曼滤波,快速同时定位与地图创建,扩展卡尔曼滤波

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Abstract:

Standard FastSLAM algorithm suffers from particle set degeneracy and accumulation errors caused by linearization of the nonlinear model. To overcome the above problems, this paper proposed a novel FastSlam algorithm based on square root unscented Kalman filterSR-UKF. SR-UKF selected a group of representative sigma points to approximate the covariance, these sigma points were propageted through the non-linearforce model to reconstruct the new statistical characteristics. Using SR-UKF to replace EKF for posteriori estimation of particles could reduce the linearization error and slow down particle set degeneracy. SR-UKF ensured the non-negative definite of covariance matrix to guarantee the stability of SLAM algorithm. The simulation experiments demonstrate that the proposed algorithm is better than FastSLAM 2. 0 both in accuracy and robustness.

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