In this paper, we present a feasible solution to the problem of autonomous navigation in initially unknown environments using a pure vision-based approach. The mobile robot performs range sensing with a unique omnidirectional stereovision system, estimates its motion using visual odometry and detects loop closures via a place recognition system as it performs topological map building and localization concurrently. Owing to the importance of performing loop closing regularly, the mobile robot is equipped with an active loop closure detection and validation system that assists it to return to target loop closing locations, validates ambiguous loop closures and provides it with the ability to overturn the decision of an incorrectly committed loop closure. A refined incremental probabilistic framework for an appearance-based place recognition system is fully described and the final system is validated in multiple experiments conducted in indoor, semi-outdoor and outdoor environments. Lastly, the performance of the probabilistic framework is compared with the rank-based framework with additional experiments conducted in the semi-autonomous mode, where the mobile robot, provided with a priori information in the form of a topological map that is built in a separate occasion in an offline manner, is required to reach its target destination.