Developing robotic systems with multiple sensors

A general approach is presented for the integration of vision, range, proximity, and touch sensory data to derive a better estimate of the position and orientation (pose) of an object appearing in the work space. Efficient and robust methods for analyzing vision and range data to derive an interpretation of input images are discussed. Vision information analysis includes a model-based object recognition module and an image-to-world coordinate transformation module to identify the three-dimensional (3-D) coordinates of the recognized objects. The range information processing includes modules for reprocessing, segmentation, and 3-D primitive extraction. The multisensory information integration approach represents sensory information in a sensor-independent form and formulates an optimization problem to find a minimum-error solution to the problem. The capabilities of a multisensor robotic system are demonstrated by performing a number of experiments using an industrial robot equipped with several sensors of differing types