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This paper presents a vision-based approach to simultaneous localization and mapping (SLAM) in indoor/outdoor environments with minimalistic sensing and computational requirements. The approach is based on a graph representation of robot poses, using a relaxation algorithm to obtain a globally consistent map. Each link corresponds to a relative measurement of the spatial relation between the two nodes it connects. The links describe the likelihood distribution of the relative pose as a Gaussian distribution. To estimate the covariance matrix for links obtained from an omnidirectional vision sensor, a novel method is introduced based on the relative similarity of neighboring images. This new method does not require the determination of distances to image features using multiple-view geometry, for example. Combined indoor and outdoor experiments demonstrate that the approach can handle different environments (without modification of the parameters), and it can cope with violations of the ldquoflat floor assumptionrdquo to some degree and scales well with increasing size of the environment, producing topologically correct and geometrically accurate maps at low computational cost. Further experiments demonstrate that the approach is also suitable for combining multiple overlapping maps, e.g., for solving the multirobot SLAM problem with unknown initial poses.