Mobile Robot Object Recognition through the Synergy of Probabilistic Graphical Models and Semantic Knowledge
José-Raúl Ruiz-Sarmiento, Cipriano Galindo, Antonio Javier Gonzalez-Jimenez · 2014
Abstract. Mobile robots intended to perform high-level tasks have to recognize objects in their workspace. In order to increase the suc-cess of the recognition process, recent works have studied the use of contextual information. Probabilistic Graphical Models (PGMs) and Semantic Knowledge (SK) are two well-known approaches for deal-ing with contextual information, although they exhibit some draw-backs: the PGMs complexity exponentially increases with the num-ber of objects in the scene, while SK are unable to handle uncer-tainty. In this work we combine both approaches to address the object recognition problem. We propose the exploitation of SK to reduce the complexity of the probabilistic inference, while we rely on PGMs to enhance SK with a mechanism to manage uncertainty. The suitabil-ity of our method is validated through a set of experiments, in which a mobile robot endowed with a Kinect-like sensor captured 3D data from 25 real environments, achieving a promising result of∼94 % of success. 1