IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 10
Monocular Visual Autonomous Landing System for Quadcopter Drones Using Software in the Loop
Figure 8.
Output of the P, PD, and PID controllers for each state in Xði¼1:5Þ
width of the platform Ow; (d) height of the platform Oh; (e) heading u.
t
tested with the aircraft hovering at a height of 3.5 m above
the landing pad.
The RANSAC algorithm was used to compute the
homography matrix H. Both SIFT and SURF used the
Manhattan distance to compute the matches between
descriptors, whereas ORB employed the Hamming distance.
Figure 7 illustrates the results of the three algorithms.
The violin plots show the error between the
observations Z and the ground truth ofthe platform. These
plots show the distribution ofthe error for the five observed
states, with the median error represented as a white dot, the
interquartile range as a broad black bar in the center of the
violin, and the lower/upper adjacent values as a thin line.
It can be seen from Figure 7(a) and (b) that the centroid
coordinates x; y of the landing platform show similar
behavior for the ORB and SIFT detectors, with a median
value close to zero. In contrast, SURF has more dispersion
in its error distribution and a median of above 200 pixels.
The best detector for the centroid coordinates is SIFT, as
it gives a more uniform distribution compared with ORB
and SURF, and most of the error values are clustered close
to zero.
Figure 7(c) presents the error in the angle u, and it can
be observed that the SIFT detector gives better performance
than the other two detectors. The errors in the width
Ow and height Oh can beseeninFigure7(d) and (e), respectively.
From this figure, it can be seen that the three detectors
have very similar behavior for both variables, although SIFT
outperforms ORB and SURF with an error distribution close
to zero and few outliers.
The SIFT detector-descriptor is better than the other
detectors for all observations Z. Although ORB shows
10
: (a) X coordinate of the centroid; (b) Y coordinate of the centroid; (c)
similar behavior to SIFT for the first three states, it has a
large set of outliers for the last two states, while SURF
gives the worst performance throughout the observation
space.
Finally, Table 1 shows the average and standard deviation
in the errors in pixels between the SIFT detector and
the Kalman filter attached to the SIFT detector. The results
demonstrate that all of the observations are substantially
improved with the Kalman filter, reducing the average
error to almost zero and decreasing the standard deviation
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV
Table of Contents for the Digital Edition of IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV
Contents
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Contents
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover2
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - 1
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IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover3
IEEE - Aerospace and Electronic Systems - May 2022 - Tutorial XV - Cover4
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