This paper presents bubble space based representation of places (nodes) in topological maps. Bubble space simultaneously provides for detailed (bubble surfaces) and holistic (bubble descriptors) representation of places. It is based on bubble memory where visual feature values and their local S-2-metric relations from robot's viewpoint are simultaneously encoded on a deformable spherical surface. Bubble surfaces extend bubble memory to accommodate varying robot pose and multiple features. They are transformed into bubble descriptors that are rotationally invariant with respect to heading changes while being computable in an incremental manner as each new set of visual observations is made. We use bubble descriptors for place learning and recognition with support vector machines in both indoor and outdoor environments and provide analysis results on recognition, recall and precision rates and time performance including a comparative study with the state-of-the-art descriptors.