## 4 Results and Discussion  
### Trajectory Generation  
Observations of the on-board position and orientation sensors were made for the entirety of each flight. The vibration isolation properties of the sensor mount allowed a high signal to noise ratio in the IMU observations to be achieved. These observations did not exceed the maximum range of the gyroscopes and accelerometers at any stage. GPS lock was also maintained by all receivers for each flight. A minimum of 5 satellites was observed by the dual frequency receiver at all times during each flight. This allowed accurate GPS position and velocity observations to be computed for each of the four flights.  
The camera to IMU calibration procedure produced results with 99% confidence intervals of 0.11\({}^{\circ}\), 0.17\({}^{\circ}\) and 0.18\({}^{\circ}\) for pitch, roll and heading. These results are consistent with the results presented in [PERSON] _et al._[41] and can be considered to have been resolved to an accuracy suitable for this application. The SfM algorithm was able to orient all of the frames from each of the four flights. However, the accuracy of the observations from images dominated by the building in the middle of the study area was reduced due to the relatively low number of SIFT features detected in these frames (Figure 5(a)). Within a forested environment, areas of bare earth will produce a similar reduction in SIFT features. The inclusion of these frames therefore represents a reasonable determination of the spatial accuracy of  
Figure 4: Locations of the individual trees manually delineated from the point cloud for use in determining the repeatability of individual tree metric measurements using the UAV-borne LiDAR data.  
a point cloud generated in these environments. A high number of SIFT features were detected in frames that included a significant area of vegetation cover, as demonstrated in Figure 5(b). These results suggest that when used in densely vegetated environments, the SIFT algorithm should provide a reliable number of features for the SfM technique to operate accurately.  
Figure 5: The detection of SIFT features (yellow crosses) within (**a**) a frame dominated by the building in the center of the study area, 1714 SIFT features were found and (**b**) a frame dominated by natural features including trees, 2959 features were found.  
Figures 6(a) and 6(b) provide a comparison between the trajectory estimates from Flight 2 by the SPKS with and without the inclusion of SfM observations. As it was not possible to determine the true pose of the system, this comparison only allows the discrepancy between the two solutions to be observed. In this case, the orientation is varied by the inclusion of the SfM solutions. Considering an error of only 0.5\({}^{\circ}\) in an orientation angle can result in an on the ground error of 0.2 \(\mathrm{m}\), these differences are highly significant and any improvement will be determined by examining the spatial accuracy of the point cloud. There is only comparatively small differences in the estimation of position which is governed by the GPS observations.  
**Figure 6.** The differences in position (**a**) and orientation (**b**) as estimated by the Sigma Point Kalman Smoother with and without the inclusion of observations of orientation generated by the SfM algorithm. A difference in orientation 0.5\({}^{\circ}\) can result in a 0.2 \(\mathrm{m}\) difference of a measured point at the nominal flying height used in this trial.