Navigation Functionalities for an Autonomous UAV Helicopter

5.3. CONCLUSION 71
vision system stopped delivering valid data because the pattern disappeared
accidentally out of the camera’s field of view. The filter continued to deliver
position information using its dead-reckoning capability until the landing
was terminated safely. This shows how sensor fusion techniques, enhance
the overall robustness of the landing procedure.
altitude [m]
1
0.8
0.6
0.4
0.2
pattern occluded by
the shade of the
helicopter
last valid vision
measurement
filter
raw vision altitude
touch down
0
418 419 420 421
time [s]
422 423 424 425
Figure 5.10: Altitude plot of an autonomous landing completed with the
vision lost before touch down.
5.3 Conclusion
In this chapter, the benefit of integrating inertial sensors with a vision
system as part of a vision based autonomous landing system for an autonomous
helicopter have been shown. The sensor fusion algorithm is based
on a Kalman filter where the inertial sensor errors are estimated using position
observation from a single camera vision system. The major benefits
in fusing inertial sensor with vision system can be summarized as resulting
in a higher frequency state estimation, lower latency velocity estimation,
more accurate attitude angle estimation and the possibility of surviving
temporary black-out in the vision system.