A new technique for Simultaneous Localization and Mapping (SLAM) is introduced. This technique was developed as a real-time, distributed, scalable implementation for heterogeneous mobile robot teams. Update efficiency and performance under variable resources is enhanced using a new strategy called Lazy Belief Propagation. The formulations and algorithms behind the implementation are described and a simulation was used to compare several SLAM algorithms. Results demonstrate an 11% improvement in map coverage in the same amount of time compared to a traditional implementation. ©2010 IEEE.