A novel loop closure detection method in monocular SLAM

This paper proposes an image appearance-based method to deal with the loop closure detection problem of monocular simultaneous localization and mapping for mobile robots. A bag-of-visual words approach is presented for building an appearance-based scene model. Subsequently, a fuzzy $$K$$-means method is proposed to build a visual vocabulary synchronously. Each image can be represented by a vector of weighted words. The similarity between images is evaluated by the scalar product between the weighted vectors. A Bayesian filter algorithm is applied to update the detection probability and an inverse image retrieval method is employed to eliminate the wrong loop closure results. The experimental results demonstrate the efficiency of our proposed method.