Visual Simultaneous Localization and Mapping (vSLAM) system is widely used by autonomous mobile robots. Most vSLAM systems use cameras to analyze surrounding environment and to build maps for autonomous navigation. For a robot to perform intelligent tasks, the built map should be accurate. Landmark features are crucial elements for mapping and path planning. In the vSLAM literature, loop closure detection is a very important process for enhancing the robustness of the vSLAM algorithms. The most widely used algorithms for loop closure detection use a single descriptor. However, the performance of the single descriptors appears to worsen as the map keeps growing. One possible solution to this problem is to use multiple descriptors and combine them as in Naive and linear combinations. These approaches, however, have weaknesses in recognizing the correct locations due to overfitting and high-bias, which hinder the generalization performance. This paper proposes the usage of ensemble learning to combine the predictions of multiple Bayesian filter models which make more accurate prediction than individual models. The proposed approach is validated on three public datasets, namely, Lip6 Indoor, Lip6 Outdoor and City Centre. The results show that the proposed ensemble algorithm significantly outperforms the single approaches with a recall of 80%, 97% and 87%, with 100% precision on the three datasets, and outperforms the Naive approach and the existing loop closure detection algorithms.