A few years ago, Schaffrin and Iz (2008) generalized the traditional Kalman filter in such a way that it could handle observation equations with errors-in-variables. This approach led to what has since become known as Total Kalman Filtering (TKF). A drawback, however, was that the usual “data snooping” techniques were no longer applicable in the same manner. Therefore, in the presence of outliers, new search techniques need to be devised in order to accommodate for those errors-in-variables with non-zero expectations. In this contribution, an attempt will be described to prepare a suitable algorithm for this purpose in the context of mobile mapping.