Active simultaneous localization and mapping (SLAM) is an important technique for mobile robots to autonomously explore and map an environment. This letter considers the problem of active SLAM with a prior topo-metric graph. Unlike existing works, we consider a more challenging scenario that there exist indistinguishable subgraphs in the prior graph and the robot does not know its initial position. In this scenario, the robot cannot plan a global path for mapping at the very beginning. To address this problem, we propose a novel framework which consists of two stages, active localization and active mapping. Our main contributions include a graph matching approach for localization and an active localization strategy. The graph matching approach is designed to reliably localize the ego position in the prior graph, by which the switch from active localization to active mapping can be determined reliably. The active localization strategy uses only the maximum likelihood state as input to reduce the computational complexity of motion policy generation. Experimental results demonstrate that our active SLAM system is able to reliably identify its ego position in the prior graph and map an environment in the presence of indistinguishable subgraphs.