Visual loop closure detection with a compact image descriptor

In this paper, we present a method for visual loop closure detection using a compact image descriptor, Gabor-Gist. In contrast to the Bag-of-Words (BoW) approach, which is dominant in recent studies of the loop closure detection problem that derives an image descriptor from locally extracted keypoint descriptors, our method relies on a single efficient image descriptor of low dimension to describe and measure similarities among images. We employ PCA to transform a high dimensional Gabor-Gist descriptor to a lower dimensional form to improve both the computational efficiency of our method and the discriminative power of the image descriptor. In addition, we use a particle filter to exploit the correlation among images in a sequence captured by the robot in the process of identifying loop closure candidates. Our method is highly scalable due to the compactness of the image descriptor and the simplicity of particle filtering. To validate our method, we used the Oxford City dataset. Our experimental results show that for this dataset, high recall (up to 87%) can be obtained at 100% precision, with only a few particles.