Sensor planning for object search

grantor: University of TorontoThis thesis studies the almost-unexplored field of sensor planning for object search. Object search is the task of efficiently searching for a given 3D object in a given 3D environment by an agent equipped with a camera for target detection and, if the environment configuration is not known, a method of calculating depth, like stereo or laser range finder. Sensor planning for object search refers to the task of selecting the sensing parameters so as to bring the target into the field of view of the camera and to make the image of the target easily detected by the available recognition algorithms. In this thesis, the task of sensor planning for object search is formulated as an optimization problem. This problem is proved to be NP-Complete, thus an approximate solution employing a one step look-ahead strategy is proposed. This approximation is equivalent to the optimal solution under certain conditions. The search region is characterized by the probability distribution of the presence of the target. The goal is to find the desired object reliably with minimum effort. The control of the sensing parameters depends on the current state of the search region and the detecting ability of the recognition algorithm. The huge space of possible sensing actions is decomposed into a finite set of actions that must be considered. In order to represent the surrounding environment of the camera and to determine efficiently the sensing parameters over time, a concept called the sensed sphere is proposed, and its construction, using a laser range finder, is derived.Ph.D