Object detection in robotic applications for real-time localization using semi-unknown objects

We investigate the pose estimation of a semi-unknown object for stereo-vision-based navigation of a mobile manipulator. A new computationally fast vision algorithm is developed to extract the object's pose at a high rate from the captured scenes. Moreover, we present a method to deal with range dependent noise characteristics of the stereo vision to fulfill requirements for mobile manipulation tasks. As shown, a smoothed, high-bandwidth feedback is obtained by using robust real-time estimation, where special care is taken to accommodate the aforementioned nonlinearities of the stereo vision. This way, the manipulator is capable of positioning itself in the close vicinity of an object by navigation of its nonholonomic mobile base. Importantly, we achieve nearly the same accuracy in mobile robot positioning compared to standard marker-based techniques at distances greater than those typically considered suitable for position-based, high-bandwidth motion control within the robotics community.