Theta-Disparity: An Efficient Representation of the 3D Scene Structure

We propose a new representation of 3D scene structure, named theta-disparity. The proposed representation is a 2D angular depth histogram that is calculated using a disparity map. It models the structure of the prominent objects in the scene and reveals their radial distribution relative to a point of interest. The proposed representation is analyzed and used as a basic attention mechanism to autonomously resolve two different robotic scenarios. The method is efficient due to the low computational complexity. We show that the method can be successfully used for the planning of different tasks in the industrial and service robotics domains, e.g., object grasping, manipulation, plane extraction, path detection, and obstacle avoidance.

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