  To measure system states and local environment directly with high precision,
expensive sensors are required. However, highly accurate system states and
environmental perception can also be achieved using data fusion techniques and
digital maps. One crucial task of multi-sensor state estimation is to project
different sensor measurements into the same temporal, spatial and physical
domain, estimate their covariance matrices as well as the exclusion of
erroneous measurements. This paper presents a generic approach for robust
estimation of vehicle movement (odometry). We will shortly present our
calibration procedure, including the estimation of sensor alignments, offset /
scaling errors, covariances / correlations and time delays. An improved
algorithm for wheel diameter estimation is presented. Additionally an approach
for robust odometry will be shown as odometry estimations are fused under known
covariances, while outliers are detected using a chi-squared test. Utilizing
our robust odometry, local environmental views can be associated and fused.
Furthermore our robust odometry can be used to detect and exclude erroneous
position estimates.
