# Landmark Generation in Visual Place Recognition Using Multi-Scale Sliding Window for Robotics
## 4 Experiments  
### Experimental Settings  
In this section, we describe the results of our comprehensive evaluation of the proposed method for landmark generation. Regarding the competing methods involved in our comparative study, we used Edge Boxes [17] and SP-Grid [34] in our comprehensive study, since they perform with a similar landmark-based mechanism. Specifically, Edge Boxes is used in a classic work [12] on landmark-based visual place recognition, and has been shown to perform competitively with respect to other landmark generation methods [28]. SP-Grid is a technique that performs multi-scale segmentation using a super-pixel approach. It is conceptually similar to our method in terms of spatial coverage of the image, but produces a non-uniform distribution of landmark size. Importantly, SP-Grid has been studied in the context of visual place recognition [34]. In addition, to quantitatively demonstrate the advantage of our model, NetVLAD [36] implemented by its best model (VGG-16 + NetVLAD + whitening, trained on Pittsburgh) was also included in the comparative study since it is a state-of-the-art deep model based approach for visual place recognition. We used four challenging datasets to evaluate the competing methods: Gardens Point Campus dataset [13], Mapillary Berlin [12], Freiburg Across Seasons [3] and Oxford RobotCar [37]. In all cases, we used one sequence as the query image set, and images in the second sequence as the reference images in turn. Specifically, we used the \"day-left\" and \"night-right\" sequences in Gardens Point Campus, which exhibit significant illumination and medium viewpoint variations. In this dataset, both of the \"day-left\" and \"night-right\" sequences contain 200 frames, while the ground-truths are defined as the images located within the four neighboring frames in two sequences. Similarly, we used Mapillary Berlin containing 157 images in query sequence and 67 images in reference sequences that exhibit large viewpoint changes and dynamic objects. Next, we used partial images from \"Summer 2015\" and \"Winter 2012\" sequences in Freiburg Across Seasons, which exhibit drastic illumination and scene variations, with slight viewpoint changes. Both two sequences contain 624 frames. Finally, we used the two subsets of Oxford RobotCar which are assembled from the right side of the Bumblebee XB3 trinocular stereo camera at 09:14 on 16 December 2014 and from the left side of the camera at 13:37 on 8 July 2015, respectively. We chose 522 images from each of these two sequences exhibiting seasonal and viewpoint changes. For the last two datasets, we ensured that there is exactly one reference image to match a query image. For all datasets, we generated 100 landmarks from each image with our method, 91 landmarks with SP-Grid, and 100 landmarks with Edge Boxes, respectively. In our study, the scale distribution of our method was set at [0.16, 0.25, 0.36, 0.49], and there were always 25 landmarks per scale. Note that the predefined scales were empirically set in our experiments, although we experimented with other scale sets with no significant difference in performance, as long as they are more or less evenly spaced. Qualitatively, the four scales we used in the experiments tend to produce a relatively accurate landmark-level matching result while maintaining discrimination between different scales. It is important to note that the landmarks generated from the scales produce appropriate overlap among the sliding windows in order to achieve the shift invariance. In particular, larger-scale landmarks usually have substantial overlap exhibiting desirable robustness to the shift invariance. By contrast, although the overlap between small-scale landmarks is reduced, their small size helps with the shift invariance property. In terms of performance metric, we used precision and recall curves [13] of the place recognition result, as is commonly practiced.