# A Visual Odometry Pipeline for Real-Time UAS Geopositioning
## 3 Methodology
### Landmark Retrieval and Matching  
Given the geoposition of a UAS, a list of landmarks is retrieved from the quad-tree data structure indexed by their geoposition. During the retrieval process, landmarks of an extended area are retrieved to reduce access to the quad-tree. As shown in Figure 5, the look-up region visualized within the aerial orthophoto overlaps with the acquired image at the UAS geoposition. We observe, however, that changes in the scene remain a significant challenge. For instance, differences in the image for a UAS flown in summer and winter using the same flight plan are shown in Figure 5b,c, where the vegetation appears significantly different. Besides seasonal differences, differences due to construction may also be observed, such as the gray building and its surrounding road network within the red box in Figure 5.  
Figure 2: (**a**) is a \(2.5\times 2.5\) km\({}^{2}\) reference orthophoto obtained from Google Earth using Google Maps API. The red rectangle box indicates the look-up region corresponding to the estimated UAS geoposition. Reference orthophoto can also be generated using aerial mapping. (**b**) Automatically extracted landmarks with known geoppositions that are stored in geospatial quad-tree data structure illustrated in (**c**).  
Figure 4: Efficiency of quad-tree compared against brute force search. It is important to note that the y-limit behaves differently in the left and right range considerations.  
Figure 3: Visualization of the quad-tree for the reference orthophoto landmarks extracted offline.  
The reference orthophoto generated from imagery is generally historical and is different from the imagery acquired during the UAS flight, and traditional landmark (keypoint) matching algorithms collectively perform poorly. We have selected Oriented FAST and Rotated BRIEF (ORB) [18] as a representative of traditional schemes and also adopted the more recent landmark detection/matching algorithm SuperGlue [3]. SuperGlue is observed to be resilient to seasonal changes and provides a more extensive set of matching landmarks from the reference orthophoto and the acquired USA image. This is illustrated in Figure 6, where the algorithm provides matches despite construction changes and seasonal differences.  
Figure 5: (a) The look-up region and landmarks from the reference orthophoto at the UAS geogosition. (b,c) UAS images acquired in summer and winter, respectively. Appearance differences can be seen due to seasonal changes and construction changes highlighted inside the red box.  
Figure 6: (a) Look-up region from the reference orthophoto superimposed with matched landmarks. (b\(\sim\)d) UAS acquired images with superimposed matched landmarks for two seasons and changes in the building layout due to construction.  
Apart from the above challenges in matching, another essential characteristic typically addressed in most of the published literature is the invariance of the matching schemes to rotation and scaling observed frequently in the UAS geopositioning problem as the platform changes altitude and attitude. We have tested ORB and SuperGlue matching performance under attitude changes (5\({}^{\circ}\)-50\({}^{\circ}\)) and altitude changes (60-100 m). As shown in Figure 7, SuperGlue performs comparably better than ORB, but its performance drops significantly after 40\({}^{\circ}\). The effect of altitude change on landmark matching is shown in Figure 7.