Localization for legged robot with single low resolution camera using genetic algorithm.

Tong, Fung Ling.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 94-96).Abstracts in English and Chinese.Abstract --- p.i摘要 --- p.iiiAcknowledgement --- p.iiiTable of Contents --- p.ivList of Figures --- p.viiList of Tables --- p.xChapter Chapter 1 - --- Introduction --- p.1Chapter Chapter 2 - --- State of the art in Vision-based Localization --- p.6Chapter 2.1 --- Extended Kalman Filter-based Localization --- p.6Chapter 2.1.1 --- Overview of the EKF algorithm --- p.6Chapter 2.1.2 --- Process of the EKF-based localization algorithm --- p.8Chapter 2.1.3 --- Recent EKF-based vision-based localization algorithms --- p.10Chapter 2.1.4 --- Advantages of the EKF-based localization algorithms --- p.11Chapter 2.1.5 --- Disadvantages of the EKF-based localization algorithm --- p.11Chapter 2.2 --- Monte Carlo Localization --- p.12Chapter 2.2.1 --- Overview of MCL --- p.12Chapter 2.2.2 --- Recent MCL-based localization algorithms --- p.14Chapter 2.2.3 --- Advantages of the MCL-based algorithm --- p.15Chapter 2.2.4 --- Disadvantages of the MCL-based algorithm --- p.16Chapter 2.3 --- Summary --- p.16Chapter Chapter 3 - --- Vision-based Localization as an Optimization Problem --- p.18Chapter 3.1 --- "Relationship between the World, Camera and Robot Body Coordinate System" --- p.18Chapter 3.2 --- Formulation of the Vision-based Localization as an Optimization Problem --- p.21Chapter 3.3 --- Summary --- p.26Chapter Chapter 4 - --- Existing Search Algorithms --- p.27Chapter 4.1 --- Overview of the Existing Search Algorithms --- p.27Chapter 4.2 --- Search Algorithm for the Proposed Objective Function --- p.28Chapter 4.3 --- Summary --- p.30Chapter Chapter 5 - --- Proposed Vision-based Localization using Genetic Algorithm --- p.32Chapter 5.1 --- Mechanism of Genetic Algorithm --- p.32Chapter 5.2 --- Formation of Chromosome --- p.35Chapter 5.3 --- Fitness Function --- p.39Chapter 5.4 --- Mutation and Crossover --- p.40Chapter 5.5 --- Selection and Stopping Criteria --- p.42Chapter 5.6 --- Adaptive Search Space --- p.44Chapter 5.7 --- Overall Flow of the Proposed Algorithm --- p.46Chapter 5.8 --- Summary --- p.47Chapter Chapter 6 - --- Experimental Results --- p.48Chapter 6.1 --- Test Robot --- p.48Chapter 6.2 --- Simulator --- p.49Chapter 6.2.1 --- Camera states simulation --- p.49Chapter 6.2.2 --- Oscillated walking motion simulation --- p.50Chapter 6.2.3 --- Input images simulation --- p.50Chapter 6.3 --- Computer for simulations --- p.51Chapter 6.4 --- Position and Orientation errors --- p.51Chapter 6.5 --- Experiment 1 一 Feature points with quantized noise --- p.53Chapter 6.5.1 --- Setup --- p.53Chapter 6.5.2 --- Results --- p.56Chapter 6.6 --- Experiment 2 一 Feature points added with Gaussian noise --- p.62Chapter 6.6.1 --- Setup --- p.62Chapter 6.6.2 --- Results --- p.62Chapter 6.7 --- Experiment 3 一 Noise reduction performance of the adaptive search space strategy --- p.77Chapter 6.7.1 --- Setup --- p.77Chapter 6.7.2 --- Results --- p.79Chapter 6.8 --- Experiment 4 一 Comparison with benchmark algorithms --- p.83Chapter 6.8.1 --- Setup --- p.83Chapter 6.8.2 --- Results --- p.85Chapter 6.9 --- Discussions --- p.88Chapter 6.10 --- Summary --- p.90Chapter Chapter 7- --- Conclusion --- p.91References --- p.9