1. A method for real time object recognition and pose estimation using in-situ monitoring, comprising the steps of: a) receiving a sequence of 2D real time images or 3D real time images or 2D and 3D real time images from an image capturing unit mounted in a robot, for each of the received 2D or 3D images, extracting single or multiple evidences of a target object to be recognized comparing the extracted evidences with model information, and generating multiple hypotheses or candidates on locations and poses of the target object as probabilistic particles in a space; b) probabilistically fusing two or more sets of the multiple hypotheses or candidates on the locations and poses of the target object, which are generated individually from the received real time images and represented as probabilistic particles in the step a), and filtering and updating inaccurate information out of the multiple hypotheses or candidates on the locations and poses through particle filtering, and finally determining a location and a pose of the target object; c) generating regions of interest (ROIs) for each of the received real time images based on the multiple hypotheses or candidates on the location and poses of the target object from the step b), and collecting and calculating real time environmental information that includes a distance from each of the generated ROIs to the robot; d) selecting probabilistically an evidence or a set of evidences optimal for use in detection based on the generated ROIs and environmental information from the step c), and proposing a cognitive action of the robot for collecting additional evidences, if more evidences are required for a final decision, based on the environmental information from the step c); and e) repeating the steps a) and b) and the steps c) and d) in parallel until a result of object recognition and pose estimation is probabilistically satisfied, wherein the steps a), b), c), d) and e) are performed by a processor.