In this paper, we present the application of our generic, sensor-independent Map Match SLAM framework to visual mapping. In our previous work [14] , we have introduced the Map Match SLAM approach for mapping with sonar range readings: Extending the grid-based Rao-Blackwellized Particle Filter SLAM approach, in Map Match SLAM, a local map is maintained by each particle in addition to the global map. The local map is used to represent the most recent observations, and weighting of the particles

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... done based on the compliance of the local and the global map. In this paper, we show how RBPF SLAM can also be applied for mapping and path reconstruction with a stereo camera or a single monocular camera, respectively. By mapping with completely different sensors such as sonar, stereo, or monocular cameras, we prove the wide range applicability of RBPF SLAM and our Map Match SLAM computational framework.