An important part of current research on appearance based mapping works towards semantic representations of the environment, which may allow autonomous systems to perform higher level tasks and provide better human–robot interaction. This work presents a new omnidirectional vision based scene labeling approach for augmented indoor topological mapping. Omnidirectional vision systems are of particular interest because they allow us to have more compact and efficient representation of the environment. Our approach includes novel ideas in order to augment the semantic information of a typical indoor topological map: we pay special attention to the semantic labels of the different types of transitions between places, and propose a simple way to include this semantic information as part of the criteria to segment the environment into topological regions. This work is built on efficient catadioptric image representation based on the Gist descriptor, which is used to classify the acquired views into types of indoor regions. The basic types of indoor regions considered are Place and Transition, further divided into more specific subclasses, e.g., Transition into door, stairs and elevator. Besides using the result of this labeling, the proposed mapping approach includes a probabilistic model to account for spatio-temporal consistency. All the proposed ideas have been evaluated in a new indoor dataset also presented in this paper. This dataset has been acquired with our wearable catadioptric vision system, showing promising results in a realistic prototype.