Relative position sensing by fusing monocular vision and inertial rate sensors.

摘要

This dissertation describes the development of a new, robust, relative-position sensing strategy suitable for unstructured and unprepared environments. Underwater manipulation is the particular application that motivated this research. Although many relative position sensing systems have already been developed, achieving the level of robustness that is required for operation in the underwater environment is very challenging.; The sensing strategy is based on fusing bearing measurements from computer vision and inertial rate sensor measurements to compute the relative position between a moving observer and a stationary object. The requirements on the vision system have been chosen to be as simple as possible: tracking a single feature on the object of interest with a single camera. Simplifying the vision system has the potential to create a more robust sensing system. The relative position between a moving observer and a stationary object is observable if these bearing measurements, acquired at different observer positions, are combined with the inertial rate sensor measurements, which describe the motion of the observer.; The main contribution of this research is the development of a new, recursive estimation algorithm which enables the sensing strategy by providing a solution to the inherent sensor fusion problem. Fusing measurements from a single bearing sensor with inertial rate sensor measurements is a nonlinear estimation problem that is difficult to solve with standard recursive estimation techniques, like the Extended Kalman Filter. A new, successful estimator design—based on the Kalman Filtering approach but adapted to the unique requirements of this sensing strategy—was developed. The new design avoids the linearization of the nonlinear system equations. This has been accomplished by developing a special system representation with a linear sensor model and by incorporating the Unscented Transform to propagate the nonlinear state dynamics.; The dissertation describes the implementation of the sensing strategy and a demonstration that illustrates how the sensing strategy can be incorporated into the closed-loop control of an autonomous robot to perform an object manipulation task. The performance of the sensing strategy is evaluated with this hardware experiment and extensive computer simulations. Centimeter-level position sensing for a typical underwater vehicle scenario has been achieved.