# Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics
## 1 Introduction  
Visual place recognition plays a critical role in both long-term robotic autonomy [1; 2; 3; 4; 5; 6; 7] and spatial cognition and cartography [8; 9]. It not only helps determine whether the current view of a robot corresponds to a place or location that has been already visited in the past [4], but also benefits the usability of landmark pictograms in the field of cartography [9]. In this paper, we focus on its application in robotics. Recently, considerable progress has been made in visual place recognition, although challenges remain, especially with regard to handling viewing condition changes and dynamic objects [10; 11]. The recognition performance of existing methods is particularly vulnerable to drastic viewpoint and illumination changes [4; 12]. In addition, seasonal changes in an outdoor environment can adversely affect the recognition performance as well [3]. Recently, taking advantage of the tremendous progress of deep learning research, the approach based on landmarks has emerged as a leading research direction in visual place recognition, and they have shown greatly improved performance in tackling viewing condition changes [1; 12; 13; 14; 15]. Methods within this approach describe a visual scene in terms of the feature vectors of the landmarks detected in the scene, and they have significantly outperformed traditional techniques based on local keypoints such as FAB-MAP [11] and VLAD (vector of locally aggregated descriptors) [16]. Landmarks are essentially image regions or bounding boxes [12], and they are typically extracted by an object proposal method [17; 18]. Subsequently, each of the landmarks is represented by a feature vector computed from the corresponding bounding box by a convolutional neural network (CNN), which can be one ofmany off-the-shelf pre-trained CNN models [19; 20; 21]. With each image represented in terms of a set of CNN feature vectors, the pairwise matching between the landmarks in two images is performed in an exhaustive manner, and the overall similarity between two images can be computed from the matched landmarks. This similarity between images serves as the basis for the final place recognition decision. Clearly, the success of landmark-based place recognition heavily depends on the quality of the landmarks detected to represent images. Existing methods rely on object proposal methods for landmark detection such as Edge Boxes [17], BING [22], and Faster R-CNN [18]. These methods, however, have been developed for the application of object detection and recognition, not that of visual place recognition. As a result, object proposal methods may not necessarily be ideal for identifying image regions as landmarks. In fact, our analysis of the landmarks detected by object proposal methods shows several characteristics that are not conducive to landmark-based recognition. First, there are often a large number of objects or landmarks of small sizes, and such landmarks are statistically less reliable for landmark matching than large-size landmarks due to the lack of semantic information in small image regions. Second, the statistical distribution of the landmark sizes can be quite uneven, skewed toward one scale for example, and this does not help achieve viewpoint invariance when scale change occurs between multiple visits to the same scene. Third, landmarks detected by an object proposal method may cover an image spatially poorly, and fail to make full use of the entire image. The above observations motivate us to investigate an alternative method of landmark generation specifically for the application of visual place recognition. In this paper, we propose and experimentally demonstrate the merit of a novel landmark generation method that improves recognition performance and reduces the computational complexity of the generation process. Our method uses a simple sliding window procedure so that the landmarks cover an image uniformly spatially.