# Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics
## 2 Related Work  
Several approaches have been proposed to solve the place recognition problem in changing environments. Traditional approaches are based on the hand-crafted features including both local features (e.g., SIFT [23]) and global features (e.g., GIST [24]). A representative method of the local features is FAB-MAP [11]. In this method, the SIFT features are extracted from images and the bag-of-words model is used to combine all the local features to represent an image. On the other hand, SeqSLAM [5] is a representative global feature method. In this approach, the global SAD features of a sequence of images are used to characterize a place, while it improves the recognition robustness and accuracy. Although these methods achieve promising results, the local features suffer from the appearance variations while global features are prone to viewpoint changes. Recently, inspired by the tremendous success of deep learning and deep representation, deep CNN features have been exploited in visual place recognition. The earliest research in using deep learning for place recognition focused on directly selecting appropriate CNN layers to extract features for global image representation [13]. In this line of research, a pre-trained CNN model was utilized to extract the features from appropriate layers for the entire images, and then the similarity between two images was calculated based on the cosine distance of these features. Due to enjoying better discrimination  
Figure 1: Three examples of the pairwise matching using our approach. Each row presents two correctly matching images while the boxes outlined with the same color in two images denote the successfully matched landmarks. It shows that the landmarks generated using our method is robust to dramatic illumination, viewpoint and seasonal changes (from **top** to **bottom**). capacity of the ConvNet feature, massive efforts are devoted to using it for replacing the traditional hand-crafted global features (e.g., SAD features in SeqSLAM [25] and HOG features in [26]) and thus achieve higher recognition accuracies. However, such a method fails to simultaneously handle environment and viewpoint variations, since the feature extracted from the whole image is lacking in viewpoint invariance. To address viewpoint invariance, CNN description has been combined with local region detectors into a landmark-based place recognition framework [14]. In a similar study [12], a set of local regions of an image was detected as landmarks by Edge Boxes [17] and described by a set of CNN feature vectors. In this case, the problem of visual place recognition is reduced to landmark matching followed by computing the overall similarity between images from the matched landmarks. These studies reveal that describing a scene by its landmark regions can achieve better invariance properties than whole-image based descriptors including invariance to the environment variations and significantly improved robustness to viewpoint changes, with the state-of-the-art performance. Due to the success of landmark-based method, many approaches were built on it for further improving the performance of visual place recognition. In general, these works operate by either introducing the extra information to landmark-based method [27] or designing efficient matching strategy for reducing computational complexity [15]. Besides, a mainstream trend emerges as proposing novel landmark generation method. Since landmark-based place recognition depends on a region or object detector, various existing methods of object detection are compared in [28]. The conventional landmark detection methods fall into two classes [29]: window-scoring and grouping-based methods. Methods in the former class, such as Edge Boxes [17], BING [22] and YOLOv2 [30], extract landmarks by using a sliding window to sample the image and identify objects based on an objectness score. In contrast, the grouping-based methods, e.g., MCG [31], Selective Search (SS) [32] and LPO [33], usually generate landmarks through the segmentation of an image.