Navigation Functionalities for an Autonomous UAV Helicopter

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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
5.2. EXPERIMENTAL RESULTS 65 Figure 5.4: Comparison between vision and filtered position data during autonomous landing.
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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
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2.1. UAV SOFTWARE ARCHITECTURE 7 wh
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2.2. THE UAV HELICOPTER PLATFORM 9
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2.2. THE UAV HELICOPTER PLATFORM 11
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
Page 31 and 32: 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
Page 55 and 56: 4.2. TRAJECTORY GENERATOR 45 by the
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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 and 68: Chapter 5 Sensor fusion for vision
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Page 71 and 72: 5.1. FILTER ARCHITECTURE 61 filter
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Page 77 and 78: 5.2. EXPERIMENTAL RESULTS 67 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 87 and 88: A.1. PAPER I 77 is a three-dimensio
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
Page 113 and 114: A.3. PAPER III 103 immediately afte
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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Page 123 and 124: 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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