Qiyuan Fan and Kin Sam Yen
Kalman filter, SLAM, mobile robot, localisation and navigation, nonlinear filtering
This paper introduces piecewise self-adjusting weighted nonlinear predictive filtering (PSAWNPF) algorithm, an enhanced extended Kalman filter (EKF) approach tailored for optimal mobile robot positioning and navigation. The algorithm uniquely incorporates quaternion-based attitude representation and a self-adjusting weighting mechanism into a conventional EKF framework. The quaternion-based attitude representation facilitates linear matrix operations, while the self-adjusting weighting mechanism dynamically adapts based on real-time error estimation, enhancing both computational efficiency and position estimation accuracy. Experimental validation on a mobile robot equipped with LiDAR and gyroscope demonstrates the superior performance of PSAWNPF- EKF, recording a 3.1% uncertainty interval for position estimation, compared to 5.3% for the unscented Kalman filter (UKF) and 6.5% for traditional EKF algorithms. The PSAWNPF-EKF algorithm also achieves a substantial 24% reduction in the average operation time for predefined routes, surpassing other algorithms. The findings affirm PSAWNPF-EKF as the most accurate and efficient option among the three algorithms for mobile robot positioning and navigation. Looking forward, future research can explore the adaptability and applicability of PSAWNPF-EKF in diverse scenarios, such as autonomous vehicle navigation and aerial drone mapping, further expanding its impact in the realm of robotics and autonomous systems.
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