Vision Based Localization and Trajectory Tracking of Nonholonomic Mobile Robots

Localization is one of the most difficult and costly problems in mobile robotics. Vision and odometry/AHRS (Attitude and Heading Reference System, three axial gyroscopes, accelerometers and magnetometers) sensors fusion strategy is prevalent in the recent years for the robot localization, due to its low cost and effectiveness in GPS-denied environments. In this thesis, a new adaptive estimation algorithm is proposed to estimate the robot position by fusing the monocular vision and odometry/AHRS sensors, and utilizing the properties of perspective projection. By the new method, the robot can be localized in real time in the GPS-denied and mapless environments, and the localization results can be theoretically proved convergent to their real values. Compared to other methods, our algorithm is simple to implement and suitable for parallel processing. To achieve the real-time performance, the algorithm is implemented in parallel using GPU (Graphics Processing Unit), and therefore it can be easily integrated into mobile robots' tasks like navigation and motion control, which need the real-time localization information. Simulations and experiments were conducted to validate the good convergence and longtime robustness performances of the proposed real-time localization algorithm.;With the developed vision based localization method as a position estimator, a new controller for trajectory tracking of the non-holonomic wheeled robot is proposed without direct position measurement. The nonholonomic motion constraint of mobile robots is fully taken into account, compared to most of existing visual sevo controllers for mobile robots. It is proved by Lyapunov theory that the proposed adaptive visual servo controller for the wheeled robot gives rise to asymptotic tracking of a desired trajectory and convergence of the position estimation to the actual position. Experiments on a wheeled robot are conducted to validate the effectiveness and robust performance of the proposed controller.;Adopting the similar idea, the new vision based localization method is once again embedded into a trajectory tracking controller for the underactuated water surface robot. It is proved once again by Lyapunov theory that the proposed adaptive visual servo controller for the underactuated water surface robot gives rise to asymptotic tracking of a desired trajectory and convergence of the position estimation to the actual position. Experiments are conducted on an underactuated water surface robot to validate the effectiveness and robust performance of the proposed controller.;The contribution of this thesis can be summarized as follows: firstly, a novel localization algorithm based on the fusion of the monocular vision and AHRS/odometry sensors is proposed. Secondly, with the former localization method embedded as a position estimator, a new controller for visually servoed trajectory tracking of the nonholonomic wheeled robot is developed. Finally, by adopting the similar strategy, this thesis proposes a new controller for visually servoed trajectory tracking of the underactuated water surface robot without direct position measurement.