Inhalt

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   Abstract
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   Zusammenfassung
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   Acknowledgement
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   Table of Contents
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   List of Figures
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   List of Tables
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   Notations
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  Chapter 1. Introduction
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   1.1. Motivation
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   1.2. Problem Statement
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   1.3. Research Work Scope
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   1.4. Related Works
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   1.5. Methodology
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   1.6. Applications
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   1.7. Thesis Overview
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   Chapter 2. Theory and Background
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   2.1. Probabilistic Motion Model
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   2.1.1. Robot Motion Model Using Wheel Odometry
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   2.2. Probabilistic Observation Model
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   2.2.1. Observation Model Using Range Sensors
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   2.3. Estimation
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   2.3.1. Extended Kalman Filter
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   2.3.2. Particle Filter
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  2.4. Localization
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   2.4.1. Pose Estimation
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  2.5. Mapping
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   2.5.1. Grid Based Mapping
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   2.5.2. Feature Based Mapping
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   1
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   2
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   2.1
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   2.2
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   2.3
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   2.4
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   2.5
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   2.5.1
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   2.5.2
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   2.5.2.1 Plane Extraction and Map Building Using a Kinect Equipped Mobile Robot
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   2.6. Navigation
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   2.6.1. Implementation
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   2.7. Summary
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  Chapter 3. Simultaneous Localization and Mapping
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   3.1. SLAM
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   3.2. Prediction
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  3.3. Correction
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   3.3.1. Clustering or Segmentation
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   3.3.2. Feature Extraction
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   3
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   3.1
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   3.2
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   3.3
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   3.3.1
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   3.3.2
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   3.3.2.1 Probabilistic Line Extraction
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   3.3.2.2 Probabilistic Plane Extraction
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   3.3.3. Feature prediction from map
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   3.3.3
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   3.3.3.1 Mahalanobis Distance
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   3.3.3.2 KD Tree
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   3.3.4. Map Update
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   3.3.5. New Features
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   3.4. Summary
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  Chapter 4. Cooperative SLAM (CSLAM)
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   4.1. SLAM for Heterogeneous features
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   4.1.1. Prediction
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   4.1.2. Update
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   4.1.3. New Plane Features
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   4.2. SLAM for Multiple robot with known initial poses
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   4.3. Summary
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   Chapter 5. Framework and Hardware Development
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   5.1. Overview of the system
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   5.1.1. Control Center
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   5.1.2. Coordinator
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   5.1.3. Agents
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  5.2. Firmware and Hardware
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   5.2.1. Firmware
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   5.2.2. Hardware
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   5.3. Simulation Environment
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   5.4. Summary
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  Chapter 6. Experiment and Results
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   6.1. Setup
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   6.2. Map Management and Feature Fusion
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   6.3. Cooperative SLAM
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   6.4. Resultant Map
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   6.5. Summary
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   Chapter 7. Discussion
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   7.1. Outlook
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   7.2. Contributions
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   7.3. Future Works
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  Appendix A
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   Encoder Velocity Error Model
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   Accelerometer Velocity Error Model
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   Gyroscope Error Model
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   Compass Angle Error Model
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  Appendix B
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   Least Square Estimation
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   Appendix C
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  Appendix D
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   Create an C# based TCP/IP Client to communicate with a simulated robot in USARSim
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   Setting up a Simulation Environment
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   Running Simulation Environment
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   Communication with game engine
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   Client Application
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   ProcessRxData
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   DisplayData
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   Important Commands and Messages Format
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  Messages
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   STA
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  SEN
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   Range Sensor
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   Laser Sensor
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   Odometry sensor
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   GPS Sensor
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   INS Sensor
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   Encoder sensor
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   Touch sensor
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   RFID sensor
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  Commands
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   INIT
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   DRIVE
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  Appendix E
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   Simulation of Map Building in ROS with Mobile Robots Equipped with Odometry and Laser Range Scanner
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   Bibliography