Ning Xian
ABSTRACT
Aiming at the problem of indoor space detection mobile robots using Particle filter SLAM algorithm, which may experience poor system stability and particle degeneracy after multiple iterations and updates, this paper proposes a Strong Tracking Cubature Particle Filter SLAM algorithm (STF-CPF-SLAM). Firstly, the Cubature Kalman (CKF) algorithm are used as the importance sampling functions of the Particle Filter algorithm (PF) to generate the mean and covariance distributions, simultaneously utilizing the fading factor of the Strong Tracking algorithm (STF) to compensate the system and enhance its robustness; Then, Strong Tracking Cubature Particle Filter algorithm is used to filter and fuse the observation data with the system model to obtain the optimized pose data of the mobile robot, thereby constructing a more accurate indoor space map; Finally, the effectiveness of the algorithm was verified through a mobile robot simulation platform. The simulation results show that the proposed algorithm reduces the error of simultaneous localization and mapping by 55.7% compared to traditional Particle Filter algorithms, verifying the feasibility and effectiveness of the algorithm, and improving the accuracy of indoor space exploration mobile robots in map construction. This algorithm provides a new reference for simultaneous localization and mapping of mobile robots.
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Index Terms
- Research of STF-CPF-SLAM algorithm for Indoor Space Detection Mobile Robot
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