UAV navigation system using line-based sensor pose estimation

Figures & data

Figure 1. Hardware components of the indoor/outdoor mapping and tracking UAV.

Figure 2. The proposed pose estimation workflow.

Figure 3. Line matching example: Real image (A) and corresponding synthetic image (B), each with 2 randomly sampled horizontal lines, sorted top to bottom (labeled as 1 and 2) and 2 randomly sampled vertical lines, sorted left to right (labeled 3 and 4). The real image lines’ corresponding virtual image line start and end points estimated by the point-to-line space resection (C).

Figure 4. York University 3D campus model.

Figure 5. UAV trajectory in the indoor environment.

Figure 6. Combining map-based and map-building localization techniques.

Figure 7. MBT / SLAM integration workflow.

Figure 8. Outdoor experiment: Tracking a 3D building model using the camera of a UAV.

Figure 9. Indoor experiment: Tracking a 3D indoor model using the camera of a UAV.

Table 1. Outdoor experiment: average () and standard deviation (σ_σ) of the pose standard deviations from the least squares adjustment for the 5 frames in Figure 8.

Method	(m)		(m)	(°)	(°)	(°)
L2L	1.9801 ± 1.7554	1.0257 ± 0.4939	1.1548 ± 0.6985	1.7651 ± 0.7624	2.5170 ± 2.3949	1.0366 ± 0.4400
P2L	1.2640 ± 1.3191	1.0775 ± 0.6008	0.8854 ± 0.3753	1.7061 ± 0.6505	1.6619 ± 1.7744	0.9746 ± 0.5706
P2P	0.2199 ± 0.0498	0.3322 ± 0.0674	0.2528 ± 0.0208	0.4761 ± 0.1109	0.3214 ± 0.0890	0.2676 ± 0.1005

Table 2. Indoor experiment: average () and standard deviation (σ_σ) of the pose standard deviations from the least squares adjustment for the 5 frames in Figure 9.

Method	(m)		(m)	(°)	(°)	(°)
L2L	0.1277 ± 0.1231	0.1273 ± 0.1278	0.1494 ± 0.1207	2.4163 ± 2.5766	1.4356 ± 1.2055	1.6718 ± 1.9057
P2L	0.0852 ± 0.0426	0.1022 ± 0.0692	0.0930 ± 0.0278	1.8315 ± 1.2989	1.0480 ± 0.6018	1.5405 ± 1.3427
P2P	0.0072 ± 0.0050	0.0068 ± 0.0034	0.0118 ± 0.0071	0.2403 ± 0.1826	0.1009 ± 0.0819	0.1815 ± 0.1436

Table 3. Outdoor experiment’s check point statistics: the averages and standard deviations of the differences between ground truth model coordinates and object coordinate calculated by the point-to-line resection for horizontal (H) lines and vertical (V) lines.

Line	No.	(m)	(m)	(m)
H	19	0.009 ± 0.240	−0.221 ± 0.440	0.026 ± 0.221
V	21	0.001 ± 0.030	−0.006 ± 0.050	−0.117 ± 1.065

Table 4. Indoor experiment’s check point statistics: The averages and standard deviations of the differences between ground truth model coordinates and object coordinate calculated by the point-to-line resection for horizontal (H) lines and vertical (V) lines.

Line	No.	(m)	(m)	(m)
H	9	−0.007 ± 0.04	0.001 ± 0.017	−0.001 ± 0.005
V	10	−0.001 ± 0.01	−0.009 ± 0.022	0.007 ± 0.090