Cholesky Factorization for Covariance Matrix Recovery
Lijuan Chen · 2012
For simultaneous localization and mapping based on sparse extended information filter,we compare the principles of nearest neighbor data association,maximum likelihood data association and joint compatibility test data association,and discuss the requirements of marginal covariance matrix recovering in data association.A computationally efficient approach based on Cholesky factorization is proposed to exactly recover the marginal covariance from information matrix.In the simulation,we compare the proposed algorithm with covariance bound approximation,and analyze three common data association approaches using the proposed algorithm in SLAM based on a sparse extended information filter.The results show that the proposed recovery algorithm is suitable for various data association approaches,leading to high localization accuracy and reduced computational complexity.Performance of different data association approaches in SEIF-SLAM are discussed.