||Simultaneous localization and mapping via Multi-vehicle Map Merging
||Department of Aeronautics & Astronautics
autonomous ground vehicle
Model Predict Control
Rao-Blackwellized Particle filter
Nowadays, the research about the area of the autonomous ground vehicle is more popular than before. Simultaneous Localization And Mapping always the first challenge we meet. However, constructing a trusty map cause a lot of time and labor consumptions. Hence it is necessary to figure out a method to speed up the mapping process. In fact, one of the significant methods is that uses multiple vehicles to explore different parts of the map. Once a vehicle can explore and merge maps autonomously, we can save lots of time. This thesis proposes a complete structure for autonomous driving including, SLAM, exploration, map merging, planning, and control. The vehicle can construct maps through the Rao-Blackwellized Particle filter and explores the unknown region according to the frontier points. After that, plan a trajectory from Timed-Elastic-Band planner, and use the Model Predict Control to track the trajectory. Finally, use feature extraction to find out the relationship between each map and merge the maps together. After integrates every module in ROS, we can have the experiment in the laboratory. Eventually, the vehicles can really explore and merge the maps autonomously.
List of figures vi
List of tables viii
List of Abbreviations ix
Chapter 1 Introduction 1
1.1 Overview 1
1.2 Related work 2
1.3 Motivation 3
1.4 Goals 4
Chapter 2 Testbed 5
2.1 Hardware 5
2.2 Software 6
Chapter 3 Rao-Blackwellized Particle Filter 8
3.1 Basic concept 8
3.2 Motion model 9
3.3 Importance weighting 13
3.4 Improved Proposal Distribution 15
3.5 Adaptive Resampling 20
3.6 Global localization 24
3.7 Simulation result 25
Chapter 4 Map Merging 31
4.1 Oriented FAST and Rotated BRIEF 31
4.2 Accerlated KAZE 35
4.3 Feature matching 37
4.4 Communication Network 39
4.5 KITTI data set 41
4.6 Parallel Tracking and Mapping 46
Chapter 5 Explore, Navigation and MPC Control 47
5.1 Frontier exploration 47
5.2 Navigation and planning 49
5.3 Model Predictive Control 50
Chapter 6 Experiment Discussion 52
6.1 Experiment setup 52
6.2 Result and Discussion 55
6.3 Conclusion 60
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