# Loop Closure Detection in RGB-D SLAM by Utilizing Siamese ConvNet Features
Xing Guangxin Xing  
2 Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China; [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (G.X.) Xing Guangxin Xing  
2 Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China; [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (G.X.) Xing Guangxin Xing  
2 Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China; [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (G.X.) Xing Guangxin Xing  
2 Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China; [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (G.X.) Xing Guangxin Xing  
2 Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China; [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (X.Z. ); [EMAIL_ADDRESS] (G.X.) Xing Guangxin Xing  
2 Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China; 2202020139@stu. extracting features as image descriptors, followed by measuring the similarity between two descriptors. Recent developments in computer vision have motivated many image descriptors for feature representations, such as Scale Invariant Feature Transform (SIFT) [8], Speeded Up Robust Features (SURF) [9], Oriented FAST and Rotated BRIEF (ORB) [10], and GIST [11], etc. These approaches are based on keypoints matching and have been widely utilized in loop closure detection. However, all the above descriptors are hand-crafted and have weak robustness to the illumination variations. For example, we applied SIFT to extract keypoints and generate descriptors for matching. Figure 2 shows matching maps of a scene with angle variations and illumination variations. As expected, the matching accuracy in Figure 2b is much lower than Figure 2a. However, the key point-based descriptor is usually invariant to affine transformation and illumination. Figure 1: Example of different scenes encountered by the robot. Figure 2: Examples of SIFT keypoints matching. To overcome the limitations of key points-based matching, in recent years, Bag-of-Visual-Words (BoVW) have been widely used in loop closure detection for visual SLAM [12; 13; 14; 15; 16]. These methods store the scene as a visual dictionary and use the K-Means algorithm to generate clusters of the extracted features, where each cluster represents a \"word\". The image is described by the histogram of each visual vocabulary instead of key point-based descriptors. Fast Appearance-Based Map (FAB-MAP) [12] is one of the typical applications of the BoVW and has become a standard baseline in loop closure detection algorithms. Fisher Vector (FV) [17; 18] and vector of locally aggregated descriptors (VLAD) [19; 20] are derived from the basic idea of BoVW and become mainstream algorithms for visual SLAM [21; 22]. Fisher vectors utilize a Gaussian Mixture Model to generate codebook and local descriptors are vector-quantized to visual words. Compared with BoVW, FV contains richer information. VLAD can be viewed as a simplification of Fisher vector and reduce the computational redundancy. In addition to the above mentioned, GIST as a global image descriptor has also been extensively applied to loop closure detection [23; 24; 25]. The GIST descriptor is computed by the Gabor filter on a whole image and the generated descriptor is no more than 1000 dimensions  
Inspired by the outstanding performance of deep learning architecture in various computer vision tasks, deep learning-based descriptors have recently come into usage in the problem of loop closure detection [26; 27; 28; 29] and the effectiveness and superiority of deep features have been validated on multiple public datasets. [PERSON] et al.