title: Systematic Error Modeling and Bias Estimation.
abstract: This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models. The results show the high performance of the proposed approach for error modeling and bias estimation.
authors.affiliation: Robotics and Embedded Systems, Technische Universität München, 80333 München, Germany. feihu.zhang@tum.de.
authors.forename: Feihu
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Robotics and Embedded Systems, Technische Universität München, 80333 München, Germany. knoll@in.tum.de.
authors.forename: Alois
authors.initials: A
authors.lastname: Knoll
keywords: bias; least square method; systematic error