An entropy based unsupervised visual vocabulary optimization for visual loop closure detection

Abstract

In the task of simultaneous localization and mapping (SLAM), a mobile robot should be able to recognize the location that it has previously visited. This is termed as the loop closure detection. Current loop closure detection is implemented by matching of image features extracted from present image and previous images, where good feature representation is highly required. Bag-of-visual words (BoW) is known to be an effect feature representation for image and video matching task. However, to build the visual feature vocabulary in the unsupervised manner is still challenging. In this paper, we are aiming at evaluating the representability of the visual words and generating the discriminative vocabulary representation. We first present an entropy ranking based scheme to improve the spectral clustering algorithm by leveraging eigenvector selection and feature embedding simultaneously. We then propose to evaluate the representative ability of the visual vocabulary quantitatively based on the complexity of the decomposition of similarity matrix in the unsupervised manner. We perform experiments on the loop closure detection datasets. which contains strong perceptual aliasing in both indoor and outdoor scenarios. Results demonstrate that the proposed BoW based feature representation has strong representation ability in measuring the similarity of loop closure events and differentiating new visited places.