# Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
## 2 Related work  
In recent years, scholars have done much research in the direction of loop closure detection algorithms based on vision. The classical algorithms can be roughly divided into two categories: the method based on the BoW (Bag of Word) [2] and the method based on the global descriptor. The first method extracts local features from the scene image and clusters them into multiple \"words\". Then the whole image is described in the form of vectors based on these \"words\". Thus, the visual loop closure detection problem is transformed into a similarity measure problem of the description vectors of the two images. BoW is the mainstream method for loop closure detection. A key problem of the BoW method is how to select local features of the image. The common feature points are SIFT [3], SURF [4] and ORB [5]. For example, [PERSON] et al. [6] used the FAST [7] operator to extract the key points and then used the SIFT [3] as feature descriptor. [PERSON] et al. [8] extracted FAST [7] key points and then calculated the descriptors using BRIEFF [9]. For the general case, the images described by the BoW can be compared one-to-one by the histogram or Hamming distance, and the closed loop is detected when the distance is less than a certain threshold. However, in a large-scale scene, search speed is very important, and some researchers have begun to apply the word tree to do efficient loop closure detection. [PERSON] et al. [10; 11] applied Chow-Liu tree approximation to describe the correlation between words and words, and then proposed the classic FAB-MAP method. [PERSON] et al. [12] made public the FAB-MAP development kit based on the work of [PERSON] et al., which provided convenience for researchers. [PERSON] et al. [13] proposed the CAT-SLAM method based on FAB-MAP, which combines loop closure detection with a local metric pose filter. Compared with FAB-MAP, the loop closure detection of CAT-SLAM is better. For the second method, the main idea is to describe the entire image with a global descriptor. [PERSON] et al. [14] proposed that color histograms provide a compact representation of an image, which results in a system that requires little memory and performs in real-time. But it is very sensitive to changes in illumination. [PERSON] et al. [15] used histograms of oriented gradients (HOG) as the feature descriptor of the image, which gave very good results for person detection in cluttered backgrounds. GIST [16] had been demonstrated to be a very effective conventional image descriptor, capturing the basic structure of different types of scenes in a very compact way. Based on this, [PERSON] et al. [17] utilized global gist descriptor computed for portions of panoramic images and a simple similarity measure between two panoramas, which is robust to changes in vehicle orientation, while traversing the same areas in different directions. Both of the two methods have their own advantages and disadvantages. [PERSON] et al. [18] proved that the global descriptor method is more sensitive to the camera pose than the BOW method. [PERSON] [19] and [PERSON] [20] proposed that the global descriptor method is more robust in the case of illumination changes. Therefore, some researchers have considered combining the two methods and proposed a method of using scene signatures. For example, [PERSON] et al. [21] presented an unsupervised system that produces broad-region detectors for distinctive visual elements, which improved the accuracy of detection. However, the features used in these methods are low-level features and designed artificially in the field of computer vision. They are sensitive to the influence of light, weather and other factors, so these algorithms lack the necessary robustness. With the disclosure of large-scale datasets (such as ImageNet [22]) and the upgrading of various hardware (such as GPU), deep learning has developed rapidly in recent years.