Abstract
For fast simultaneous localization and mapping (FastSLAM) problem, to solve the problems of particle degradation, the error introduced by linearization and inconsistency of traditional algorithm, an improved algorithm is described in the paper. In order to improve the accuracy and reliability of algorithm which is applied in the system with lower measurement frequency, a new decomposition strategy is adopted for a posteriori estimation. In proposed decomposition strategy, the problem of solving a 3-dimensional state vector and N 2-dimensional state vectors in traditional FastSLAM algorithm is transformed to the problem of solving N 5-dimensional state vectors. Furthermore, a nonlinear adaptive square root unscented Kalman filter (NASRUKF) is used to replace the particle filter and Kalman filter employed by traditional algorithm to reduce the model linearization error and avoid solving Jacobian matrices. Finally, the proposed algorithm is experimentally verified by vehicle in indoor environment. The results prove that the positioning accuracy of proposed FastSLAM algorithm is less than 1 cm and the azimuth angle error is 0.5 degrees.
Highlights
In simultaneous localization and mapping (SLAM), vehicle uses the carried sensors to sense surroundings and uses the sensed information to create environment map on one hand
The results prove that the positioning accuracy of proposed FastSLAM algorithm is less than 1 cm and the azimuth angle error is 0.5 degrees
To overcome the shortcomings of traditional adaptive filter, we developed a new nonlinear adaptive square root unscented Kalman filter (NASRUKF) which can be used in nonlinear or linear system for multisensory data fusion with uncertain process noise [15, 16]
Summary
In simultaneous localization and mapping (SLAM), vehicle uses the carried sensors to sense surroundings and uses the sensed information to create environment map on one hand. The FastSLAM based on RBPF suffers from some drawbacks such as particle degeneration, Jacobian Matrix solving and linear processing of nonlinear function [4,5,6]. To deal with these problems, the SLAM based on square root unscented Kalman filter (SRUKF) is proposed. To prevent divergence and to improve the practicability of SLAM algorithm, the so-called adaptive filtering approach has been used in SLAM algorithm to dynamically adjust the parameters of the supposedly optimum filter based on estimating the unknown parameters for online estimation of motion and by estimating the signal and noise statistics from the available data.
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