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Shaojie_Shen_Dissertation博士论文.pdf
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详细说明：香港科技大学沈劭劼老师的博士论文，十分有参考价值！ABSTRACT
AUTONOMOUS NAVIGATION IN COMPLEX INDOOR AND OUTDOOR
ENVIRONMENTS WITH MICRO AERIAL VEHICLES
Shaojie sher
ⅵ ijay Kumar
Nathan michael
Micro aerial vehicles(MAvs)are ideal platforms for surveillance and search and rescue
in confined indoor and outdoor environments due to their small size superior mobil
ity, and hover capability. In such missions, it is essential that the MAv is capable of
autonomous flight to minimize operator workload. Despite recent successes in commer-
cialization of GPS-based autonomous MAVs, autonomous navigation in complex and
possibly GPS-denied environments gives rise to challenging engineering problems that
require an integrated approach to perception, estimation planning, control and high level
situational awareness. Among these, state estimation is the first and most critical compo-
nent for autonomous flight, especially because of the inherently fast dynamics of MAVs
and the possibly unknown environmental conditions. In this thesis, we present method
ologics and system designs, with a focus on state estimation, that cnablc a light-wcight
off-the-shelf quadrotor mav to autonomously navigate complex unknown indoor and
outdoor environments using only onboard sensing and computation. We start by de
veloping laser and vision-based state estimation methodologies for indoor autonomous
flight. We then investigate fusion from heterogeneous sensors to improve robustness and
enable operations in complex indoor and outdoor environments. We further propose es
timation algorithms for on-the-fly initialization and online failure recovery. Finally, we
present planning, control, and environment coverage strategies for integrated high-level
autonomy behaviors. Extensive online experimental results are presented throughout the
thesis. We conclude by proposing future research opportunities
Contents
Introduction
1.1 Research problems
1.1.1 Autonomous flight in gPs-denied environments
1. 1.2 Multi-Sensor Fusion for Autonomous Flight
1.1.3 Estimator Initialization and Failure Recovery
1. 1. 4 Planning and control
1.1.5 Autonomous Environment Coverage
1.2 Thesis overview
5
1.3 Overview of Experimental Platforms
1. 4 Thesis contributions
2 Scientific Background and Literature review
13
2.1 Autonomous Flight in GPs-denied Environments
13
2.2 Incremental motion estimation
14
2.3 Simultaneous Localization and Mapping
15
4 Multi-Sensor fusion
2.5 Monocular Visual-Incrtial Statc Estimation
18
2.6 Estimator Initialization and Failure Recovery
2.7 Autonomous Environment Coverage
3 Laser-Based Autonomous Indoor Navigation
24
3.1 Pose estimation
25
3.1. 1 Assumption
26
3.1.2 2D Pose estimation
26
3.1.3 Altitude estimation
27
3.2 EKF-based Sensor Fusion for Control
28
3.3 Simultaneous Localization and Mapping
29
3.3.1 Environment Representation
30
3.4 Experimental Results
..31
3. 4.1 Evaluating Estimator Performance
31
3.4.2 Navigation in Confined Multi-Floor Indoor Environments
31
3.4.3 Large Scale Mapping Across Multiple Floors
.36
3.4.4 Public demonstration
,37
3.5 Discussion
.37
4 Vision-Based State Estimation and Autonomous Flight
40
4.1 Feature Detection and tracking
42
4.2 Pose estimation
4.2.1 Orientation estimation
.43
4.2.2 Position estimation
44
4.3 Mapping
46
4.3.1 Local Map Update
47
4.3.2 Scale Recovery
48
4.3.3 Global Mapping
.49
4.4 UKF-Based Sensor fusion
50
4.5 Expcrimcntal Results
51
4.5.1 Autonomous Trajectory Tracking with Ground Truth
52
4.5.2 High Speed Straight Line Tracking
..52
4.5.3 Navigation of Indoor environments with Large loops
54
4.5.4 Autonomous Navigation in Complex Outdoor Environments
57
4.6 Discussion
59
5 Multi-Sensor Fusion for Indoor and Outdoor operations
60
5.1 Multi-Sensor System Model
61
5.1.1 Absolute measurements
62
5.1.2 Rclativc mcasurcments
62
5.2 UKF-based Multi-Sensor fusion
·······
63
5.2.1 State Augmentation for Multiple Relative Measurements
64
5.2.2 Statistical Linearization for UKF
65
5.2.3 State propagation
5.2.4 Measurement Update
67
5.2.5 Delayed and Out-of-Order Measurement Update
5.3 Handling global pose measurements
5.4 Implementation details
5. 4 1 Absolute measurements
.72
5.4.2 Relative Measurement- Laser Odometry
74
5.4.3 Relative Measurement- Visual Odometry
75
5.5 Experimental Results
75
5,5. 1 Evaluation of estimator performance
76
5.5.2 Autonomous Flight in Indoor and Outdoor environments
76
5.5.3 Autonomous Flight in Tree-Lined Campus
77
5.6 Benefits and limitations
80
5.7 Discussion
82
6 Initialization and Failure Recovery for monocular Visual-lnertial Systems 84
6. 1 Linear Sliding Window VINS Estimator
85
6.1.1 Formulation
86
6.1.2 Linear rotation estimation
87
6.1 3 Linear sliding window estimator
,88
6.1. 4 IMU Measurement model
89
6.1.5 Camera Measurement model
90
6.2 Nonlinear Optimization
91
6.2.1 Formulation
91
6.2.2 IMU Measurement Model
93
6.2.3 Camera Measurement model
96
6.3 Handling Scalc Ambiguity via Two-Way Marginalization
97
6. 4 Initialization and Failure recovery
..99
6.5 Simulation results
102
6.6 Experimental Results
104
6.6.1 Real-Time Implementation
,I04
6.6.2 Implementation Details and Choice of Parameters
,105
6.6.3 Initialization performance
107
6.6.4 Autonomous Hovering
110
6.6.5 Autonomous Trajectory Tracking
111
6.6.6 Autonomous flight in Indoor environments
116
6.6.7 State Estimation in Large-Scale Environments
.120
6.7
scussion
,126
7 Planning and Control
127
7. 1 Feedback Control
127
7.2 High Level Planning
128
7.3 Minimum Jerk Trajectory generation
129
7.4 Experimental Results
131
8 Autonomous Three-Dimensional Environment Coverage
134
8.1 Motivation
135
8.2 Overview
136
8.2.1 Notes on notation
,136
8.2.2 Approach
138
8.2.3 Assumptions
140
8.3 The SDEE Algorithm
140
8.3.1 Particle-based Representation of Free Space
140
8.3.2 Resampling
141
8.3.3 Particle Dynamics
142
8.3.4 Frontier Extraction
144
8.3.5 Goal Queuing and Algorithm Termination
..147
8.3.6 Heuristics for Improved Performance
147
8.4 Complexity
148
8.5 Results
l49
8.5.1 Comparison to Frontier-based Exploration
149
8.5.2 Simulation results
.150
8.5.3 Experimental Results
.153
8.6 Discussion
.157
9 Conclusion and future Work
158
9. 1 Summary of Contributions
.159
9.2 Future Work
159
Bibliography
162
LList of tables
1 Comparison of different experimental platforms
6.1 Computation breakdown of monocular VINS
,,,,105
6.2 Convergence of nonlinear optimization
110
6.3 Trajectory tracking with varying speeds
116
6.4 Statistics of autonomous indoor fight
..,117
8.1 Complexity of the hn iteration of the sdee algorithm
148
8.2 Simulation and experimental results parameters
150
8.3 Simulation performance via duration, path length, and coverage
153
List of Figures
Flow of topics and structure of a navigation system
1.2 List of platforms
2.1 Comparison of state estimation approaches
20
3.1 Diagram of laser -based state estimation
25
3.2 Laser-based altitude estimation
28
3.3 Laser-based trajectory tracking
32
3.4 Laser-based hovering
3.5 Maps generated during laser-based navigation
34
3.6 Images of laser-based navigation
35
3.7 Map generation across three floors of an indoor environment
.36
3. 8 Public demonstration
38
4.1 Diagram of the proposed vision-based state estimator
4.2 Camera geometry notaion
4.3 Data structure for feature storage
,,,.45
4.4 Localization error distribution
46
4.5 Visual scale changes during flight
..49
4.6 Vision-based trajectory trackin
.53
4.7 Snapshots of vision-based trajectory tracking
4.8 Vision-based high speed line tracking
55
4.9 3D map from indoor experiment
56
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