Navigation Functionalities for an Autonomous UAV Helicopter

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58 CHAPTER 5. SENSOR FUSION FOR VISION BASED LANDING competences in several disciplines such as image processing, sensor fusion and control. Paper III describes the approach and solution to the complete problem. This chapter focuses on the sensor fusion problem involved in the vision based autonomous landing mode. Details of the image processing and control strategy are not described here. The reader interested in the details of these problems should read Paper III in the appendix of this thesis. The motivations for the development of a vision based landing mode are of two categories: scientific and technical. The scientific motivation is that a helicopter which does not rely on external sources of information (like GPS) contributes to the scientific goal of a self-sufficient autonomous system. The technical motivation is that GPS technology is generally not robust while operating close to obstacles. For example, in an urban environment the GPS signal can be obscured by buildings or corrupted by multi path reflections or nearby radio frequency transmitters. The landing approach proposed in Paper III is completely independent of a GPS, so it can be used for landing the helicopter in proximity of obstacles found in urban environments. In order to stabilize and control a UAV helicopter, an accurate and reliable state estimation is required. The standard strategy to solve this problem is to use several sensors with different characteristics, such as inertial sensors and GPS, and fuse them together using a Kalman filter. The integration between inertial sensors and GPS is a common practice and an extensive literature on this topic is available. Several approaches to this problem can be found in [13, 25, 23]. The method used here to fuse vision data with inertial sensors is similar to that used for GPS and inertial sensor integration with a number of differences in the implementation. The great experience gained in many successful experimental landings with our RMAX platform provides strong confirmation that the same sensor integration technique used for GPS and inertial sensors can be used when the GPS is replaced with a suitable image processing system. The vision based landing problem for an unmanned helicopter has been addressed by other research groups, some related work on this problem can be found in Paper III. As already mentioned, the landing problem is solved by using a single camera mounted on a pan-tilt unit and an inertial measurement unit (IMU)
composed of three accelerometers and three gyros. An artificial pattern is placed on the ground and it is used by the image processing system to calculate the pose of the helicopter. A picture of the pattern is shown in Fig. 5.1. Figure 5.1: Pattern used for the vision based autonomous landing. Vision and inertial sensors are combined together because of their complementarity. Vision provides drift-free position data, while inertial sensors provide position, velocity and attitude information at higher frequency but affected by drift. In fact position, velocity and attitude angles derived from inertial data alone experience unbounded error growth in time. Depending on the price of the sensor, the drift of the inertial sensor can be more or less large. A very expensive inertial navigation unit allows an airplane to navigate for minutes or even hours without large drift. Usually military and civilian aircrafts or military submarines are equipped with such sensors. Small UAVs like the RMAX cannot be equipped with such accurate sensors. The reason is that the high costs of these sensors would make the platform too expensive. A second reason is that a small UAV has limited payload capacity and accurate sensors are usually quite heavy to be carried on-board a small UAV. This is the reason why for small UAVs it is common practice to fuse together several relatively cheap sensors which have different characteristics. Fig. 5.2 shows a classification of inertial sensor performance. The data is taken from [23]. On the other hand, a vision based landing approach which relies only on a vision system data suffers of several problems. The vision system is sensitive to illumination conditions such as sun reflection or shadows which 59
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Linköping Studies in Science and T
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Acknowledgements This work would ha
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Contents 1 Introduction 1 2 Overvie
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Chapter 1 Introduction An Unmanned
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of the environment in order to avoi
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Chapter 2 Overview This chapter pre
Page 17 and 18: 2.1. UAV SOFTWARE ARCHITECTURE 7 wh
Page 19 and 20: 2.2. THE UAV HELICOPTER PLATFORM 9
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Page 23 and 24: Chapter 3 Simulation 3.1 Introducti
Page 25 and 26: 3.2. HARDWARE-IN-THE-LOOP SIMULATIO
Page 27 and 28: 3.3. REFERENCE FRAMES 17 3.3 Refere
Page 29 and 30: 3.4. THE AUGMENTED RMAX DYNAMIC MOD
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Page 35 and 36: 3.5. SIMULATION RESULTS 25 Figure 3
Page 37 and 38: 3.5. SIMULATION RESULTS 27 Figure 3
Page 39 and 40: Chapter 4 Path Following Control Mo
Page 41 and 42: 4.2. TRAJECTORY GENERATOR 31 Figure
Page 43 and 44: 4.2. TRAJECTORY GENERATOR 33 Fig. 4
Page 45 and 46: 4.2. TRAJECTORY GENERATOR 35 be use
Page 47 and 48: 4.2. TRAJECTORY GENERATOR 37 4.2.3
Page 49 and 50: 4.2. TRAJECTORY GENERATOR 39 radius
Page 51 and 52: 4.2. TRAJECTORY GENERATOR 41 Calcul
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Page 61 and 62: 4.4. EXPERIMENTAL RESULTS 51 Up [m]
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Page 65 and 66: 4.5. CONCLUSIONS 55 Vel [m/s] 20 18
Page 67: Chapter 5 Sensor fusion for vision
Page 71 and 72: 5.1. FILTER ARCHITECTURE 61 filter
Page 73 and 74: 5.1. FILTER ARCHITECTURE 63 north,
Page 75 and 76: 5.2. EXPERIMENTAL RESULTS 65 Figure
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Page 81 and 82: 5.3. CONCLUSION 71 vision system st
Page 83 and 84: BIBLIOGRAPHY 73 [9] E. Frazzoli. Ro
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Page 89 and 90: A.1. PAPER I 79 The reference point
Page 91 and 92: A.1. PAPER I 81 Path Av Err Max Err
Page 93 and 94: A.2. PAPER II 83 From Motion Planni
Page 107 and 108: A.3. PAPER III 97 A.3 Paper III Aut
Page 109 and 110: A.3. PAPER III 99 the projection of
Page 111 and 112: A.3. PAPER III 101 yes Tbo > Tlim2
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Page 115 and 116: A.3. PAPER III 105 altitude [m] 20
Page 117: A.3. PAPER III 107 Fig. 10. Flight
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Department of Computer and Informat
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No 598 Rego Granlund: C 3 Fire - A
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No 1024 Aleksandra Tešanovic: Towa
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Navigation Functionalities for an Autonomous UAV Helicopter

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