A VISION SENSOR FOR BUILDING 3-D MODELS OF STRUCTURED ENVIRONMENT

In this paper a monocular system that builds a 3-D environment model is presented. Features, such as edges and their combinations, are extracted from image frames and tracked over sequences of images. Kalman filters are used to estimate both the motion of the camera and the structure of the environment. New features are integrated to the environment model during the operation.

The focus of attention has been on modeling the uncertainties correctly. In this way the converge rate can be speeded up safely. Another main point has been a high frame rate without any special hardware. This needs very fast and efficient dynamic image processing. The measurements are performed as fast as possible with high enough accuracy and confidence. Better measurements are given more weight in the model updating than more uncertain ones.

The operation of the system has been demonstrated in real time with a manipulator that picks randomly situated blocks from a table. More complicated scenes have also been used. In another approach, 3-D industrial pallet scenes have been modelled. The appearance of the pallets can also be utilized.