A family of Kalman-type filters that estimate the user's position indoors, using range measurements and floor plan data, is presented. The floor plan information is formulated as a set of linear constraints and is used to truncate the Gaussian posterior probability densities occurring in the Kalman-type filters. The truncated Gaussian is approximated with a new Gaussian density such that the mean and the covariance matrix coincide approximately with the mean and the covariance matrix of the truncated Gaussian. The linear constraints may be used all at once or imposed one after another. Both strategies are studied in this paper and their advantages and disadvantages are discussed. Simulation data has been processed using the Extended Kalman filter, the second order Extended Kalman filter and the Unscented Kalman filter, and the results are compared to a reference solution computed with a sequential Monte Carlo particle filter. The simulations indicate that the proposed method for using floor plan information increases the accuracy of the estimates without significantly increasing the computation or the memory requirements. The low computational demand and the absence of the training phase make it a useful addition to existing indoor positioning systems.