Land vehicle localization and navigation mainly relies on the Global Positioning System (GPS)/Inertial Navigation System (INS) integration. In this paper, we propose a unified incremental regression framework that enables vehicle localization with high accuracy in urban environment. Within the framework, we propose a nonlinear Gauss Process Regression (GPR) approach to perform vehicle position prediction during GPS outages. By mapping nonlinear data into high-dimensional space by kernel function, the proposed GPR based approach is able to deal with the nonlinearity issue in GPS denied environm...
