To Örebro University

Autonomous agents, situated in real-world scenarios, need to maintain consonance between the perceived world (through sensory capabilities) and their internal representation of the world in the form of symbolic knowledge. An approach for modeling such representations of objects is through the concept of perceptual anchoring, which, by definition, handles the problem of creating and maintaining, in time and space, the correspondence between symbols and sensor data that refer to the same physical object in the external world.

The work presented in this thesis leverages notations found within perceptual anchoring to address the problem of real-world semantic world modeling, emphasizing, in particular, sensor-driven bottom-up acquisition of perceptual data. The proposed method for handling the attribute values that constitute the perceptual signature of an object is to first integrate and explore available resources of information, such as a Convolutional Neural Network (CNN) to classify objects on the perceptual level. In addition, a novel anchoring matching function is proposed. This function introduces both the theoretical procedure for comparing attribute values, as well as establishes the use of a learned model that approximates the anchoring matching problem. To verify the proposed method, an evaluation using human judgment to collect annotated ground truth data of real-world objects is further presented. The collected data is subsequently used to train and validate different classification algorithms, in order to learn how to correctly anchor objects, and thereby learn to invoke correct anchoring functionality.

There are, however, situations that are difficult to handle purely from the perspective of perceptual anchoring, e.g., situations where an object is moved during occlusion. In the absence of perceptual observations, it is necessary to couple the anchoring procedure with probabilistic object tracking to speculate about occluded objects, and hence, maintain a consistent world model. Motivated by the limitation in the original anchoring definition, which prohibited the modeling of the history of an object, an extension to the anchoring definition is also presented. This extension permits the historical trace of an anchored object to be maintained and used for the purpose of learning additional properties of an object, e.g., learning of the action applied to an object.