Abstract
In this study, the measurement update step of the Extended Kalman Filter (EKF)-based Simultaneous Localization and Mapping (SLAM) is improved. The computational complexity of the measurement uncertainty matrix inversion operation in the measurement update step is reduced via using Jacobi iteration method. It is observed that, the calculation of the measurement uncertainty matrix inverse by using Jacobi iteration method generates numerically more stable results than naive single and batch update operations. Moreover, it produces more accurate results than the results of Cholesky decomposition with less complexity.
Keywords
Cholesky decomposition
Extended Kalman filter
Simultaneous localization and mapping
Inversion (geology)
Computer science
Kalman filter
Minimum degree algorithm
Algorithm
Inverse
Computational complexity theory
Matrix decomposition
Covariance matrix
Matrix (chemical analysis)
Mathematical optimization
Mathematics
Robot
Incomplete Cholesky factorization
Artificial intelligence
Mobile robot
Subject Areas
Robotics and Sensor-Based Localization
·Aerospace Engineering ·Physical Sciences
Indoor and Outdoor Localization Technologies
·Electrical and Electronic Engineering ·Physical Sciences
Inertial Sensor and Navigation
·Aerospace Engineering ·Physical Sciences
Citations by Year
2017: 1
2018: 1
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