Bathymetric simultaneous localization and mapping (SLAM) holds high potential for accurate navigation in long endurance autonomous underwater vehicle (AUV) missions. Due to nearly flat seabed topography, the occurrence of invalid loop closures is unavoidable in bathymetric SLAM. Thus, invalid loop closure identification is a crucial component of bathymetric SLAM, a procedure that normally requires a substantial amount of computational resources. An efficient bathymetric SLAM method considering invalid loop closures is proposed in this article, which identifies invalid loop closures via PF-Backend, a robust back end method based on particle filtering theory. Using this set-up, all beliefs of historical loop closures are estimated by a small number of particles and, for each particle, the corresponding map consistency calculated via a global graph optimizer is applied as the measurement update in the particle filter. A loop closure solidification procedure is also discussed to decrease the number of particles in the PF-Backend. The new algorithm is shown to be efficient and capable of providing accurate location and mapping results using play-back experiments.