Navigation Functionalities for an Autonomous UAV Helicopter

64 CHAPTER 5. SENSOR FUSION FOR VISION BASED LANDING
5.2 Experimental results
The results presented in this section show the benefits resulting from the
sensor fusion technique used in the vision based landing system.
The 12-state Kalman filter has been implemented in the C-language
in the software architecture and runs at 50Hz. The measurement update
from the vision system is 20Hz. When a new measurement from the vision
system is available, the filter performs an update step. The accelerometers
and gyros data are sampled at 50Hz although their output is at higher
frequency. In Fig. 5.3 the available sensor characteristics are listed.
Figure 5.3: Available characteristics of the sensors involved in the sensor
fusion techniques used in the vision based landing system.
The sensors used to validate the filter results are a GPS system (centimeter
accuracy) and the Yamaha Attitude System (YAS) for the attitude
angles (around 2 deg accuracy).
The plots from Fig. 5.4 to Fig. 5.9 show flight-test data from an autonomous
vision based landing. In this particular test, the landing procedure
starts around 910 sec and finishes with the touch-down around 965
sec.
Figure 5.4 shows the comparison between the filter and the raw vision
measurements.
Figures 5.5, 5.6 and 5.7 show the velocity components calculated by the
filter compared with the GPS velocity. The upper plots of the 3 figures
show an attempt at deriving the velocity from the raw vision position. The
resulting velocity is quite noisy at the beginning of the landing due to the
large distance from the pattern (around 15 meters). As is shown in Paper
III, the errors of the vision system are larger when the pattern is far from the
helicopter. From the plots, it can be observed that as soon as the helicopter
approaches the pattern the velocity derived from the vision position is less