Modelling observation correlations for active exploration and robust object detection

Abstract

Today, mobile robots are expected to carry out increasingly complex tasks in multifarious, realworld environments. Often, the tasks require a certain semantic understanding of the workspace. Consider, for example, spoken instructions from a human collaborator referring to objects of interest; the robot must be able to accurately detect these objects to correctly understand the instructions. However, existing object detection, while competent, is not perfect. In particular, the performance of detection algorithms is commonly sensitive to the position of the sensor relative to the objects in the scene. © 2012 AI Access Foundation. All rights reserved.