JOURNAL ARTICLE
SLAM based on intelligent unscented Kalman filter
Abstract
The performance of SLAM based on unscented Kalman filter (UKF-SLAM) and thus the quality of the estimation depends on the correct a priori knowledge of process and measurement noise. Imprecise knowledge of these statistics can cause significant degradation in performance. In this paper, the adaptive Neuro-Fuzzy has been implemented to adapt the matrix covariance process of UKF-SLAM in order to improve its performance.
Keywords:
Kalman filter Unscented transform Computer science Extended Kalman filter Covariance matrix Noise (video) Artificial intelligence A priori and a posteriori Simultaneous localization and mapping Fast Kalman filter Control theory (sociology) Process (computing) Fuzzy logic Computer vision Algorithm Mobile robot Robot Control (management)
Metrics
2
Cited By
0.62
FWCI (Field Weighted Citation Impact)
21
Refs
0.80
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Is in top 1%
Is in top 10%
Citation History
Topics
Robotics and Sensor-Based Localization
Physical Sciences → Engineering → Aerospace Engineering
Target Tracking and Data Fusion in Sensor Networks
Physical Sciences → Computer Science → Artificial Intelligence
Inertial Sensor and Navigation
Physical Sciences → Engineering → Aerospace Engineering
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