Sensor models directly influence the efficiency and robustness of the estimation processes used in robot and object localization. This paper focuses on a probabilistic range finder sensor model for dynamic environments. The dynamic nature results from the presence of unmodeled and possibly moving objects and people. The goal of this paper is twofold. First, we present experiments to validate the Rigorously Bayesian Beam Model (RBBM), a new model we proposed in a previous paper. Second, we propose a sample-based full scan model to improve the state of the art models. In contrast to these Gaussian-based state of the art full scan models, the proposed model is able to handle the multi-modality of the range finder data, which is shown here to occur even in simple static environments.