In this paper, we provide a systematic study of the task of sensor planning for object search. The search agent's knowledge of object location is encoded as a discrete probability density which is updated whenever a sensing action occurs. Each sensing action of the agent is defined by a viewpoint, a viewing direction, a field-of-view, and the application of a recognition algorithm. The formulation casts sensor planning as an optimization problem: the goal is to maximize the probability of detecting the target with minimum cost. This problem is proved to be NP-Complete, thus a heuristic strategy is favored. To port the theoretical framework to a real working system, we propose a sensor planning strategy for a robot equipped with a camera that can pan, tilt, and zoom. In order to efficiently determine the sensing actions over time, the huge space of possible actions with fixed camera position is decomposed into a finite set of actions that must be considered. The next action is then selected from among these by comparing the likelihood of detection and the cost of each action. When detection is unlikely at the current position, the robot is moved to another position for which the probability of target detection is the highest.
Works done by Ye and Tsotsos REF tackle the field of sensor planning for 3D object search.
Sensor Planning for 3D Object Search
CVIU
Computer Science Mathematics