By incorporating known floor-plans in sequential Bayesian positioning estimators such as Particle Filters (PF), long term positioning accuracy can be achieved as long as the map is sufficiently accurate and the environment sufficiently constraints pedestrians' motion. Instead of using binary decisions to eliminate particles when crossing a wall as several authors do, a maps-based angular probability density function (PDF) is used in this paper that is capable of weighting the possible headings of the pedestrian according to local infrastructure. We will show that the angular PDF will help to obtain better performance in critical multi-modal navigation scenarios.