Vision-Based Mobile Robot Navigation And Environment Modeling

Powerful applications of the networked robotic system greatly depend on intelligent behavioral abilities of the mobile robot itself. For more powerful applications of networked robotic system, researches on the autonomous localization, navigation, and environmental modeling of mobile robot in feature-based indoor environments are presented in this dissertation.Description and recognition of features, normally extracted from sonar or vision sensors, are fundamental for robot navigation. To avoid the poor characteristics of a single sonar data, sonar line features are extracted to improve performance. And as polyhedron features prevail in office indoor environments, Bayesian networks recognition method is introduced which combines color models and perceptual organization, and recognition results of typical features indoor are given. Additionally, 2D planar line features are extracted with a simple and practicable tracking navigation system realized.On one hand, global localization is the most important behaviors for autonomous navigation of robot, for which robustness and precision a necessity. To enhance the performance of Markov and Kalman filter for application, Markov-EKF is proposed for robust global localization and precise pose tracking. Path tracking experiment validates the performance of Markov-EKF method with localization error comparison with Markov method given. In order to improve the localization performance in uncertain or un-modeled conditions, as well as to resolve the kidnapped problem, vision-based extended Monte Carlo algorithm is proposed. Localization experiments demonstrate the ability to deal with the un-modeled events.On the other hand, Environmental modeling on-line—SLAM(Simultaneous Localization and Mapping) is another important problem for mobile robot. A hybrid hierarchical mapping method is proposed which integrates topological and feature-based method to extend to large scale and cyclic environments, thus reducing the computation complexity and improving the precision of environment model. Experimental results in typical environments, small simulating cyclic environment, small real environment and large scale environment, all demonstrate the validity of the hierarchical method.Example applications of the intelligent behaviors above to networked robotic system, such as trajectory tracking, global localization, and topological mapping, validate the feasibility and effectiveness of integration and approaches.