Joint bias estimation and localization in factor graph

Feihu Zhang, Daniel Malovetz, Dhiraj Gulati, Daniel Clarke, Alois C. Knoll

Abstract: This paper describes a new approach for cooperative localization by using both internal and external sensors. In contrast to the state-of-the-art methods, the proposed approach analyses the statistical properties of the systematic error during the transformation phase. A factor graph is formulated which jointly estimates both the biases and the locations. The proposed approach is evaluated by using simulated data from odometry, GPS and radar measurements. The experiment demonstrates excellent performance of the proposed approach in comparison to traditional techniques.