Multipath-based simultaneous localization and mapping (MP-SLAM) is a promising approach to obtain position information of transmitters and receivers as well as information regarding the propagation environments in future mobile communication systems. Usually, specular reflections of the radio signals occurring at flat surfaces are modeled by virtual anchors (VAs) that are mirror images of the physical anchors (PAs). In existing methods for MPSLAM, each VA is assumed to generate only a single measurement. However, due to imperfections of the measurement equipment such as noncalibrated antennas or model mismatch due to roughness of the reflective surfaces, there are potentially multiple multipath components (MPCs) that are associated with one single VA. In this paper, we introduce a Bayesian particle-based sum-product algorithm (SPA) for MP-SLAM that can cope with multiple-measurements being associated to a single VA. Furthermore, we introduce a novel statistical measurement model that is strongly related to the radio signal. It introduces additional dispersion parameters into the likelihood function to capture additional MPC-related measurements. We demonstrate that the proposed MP-SLAM method can robustly fuse multiple measurements per VA based on numerical simulations.