Active visual object search using affordance-map in real world: A human-centric approach

Abstract

Human context is the most natural explanation why objects are placed and arranged in a particular order in an indoor environment. Usually, humans arrange objects in order to support their intended activities in a given environment. However, most of the common approaches for robotic object search involve modelling object-object relationships. In this paper, we hypothesize such relationships are centered around humans and bring human context to object search by modelling human-objects relationships through affordance-map. It identifies locations in a 3D map which support a particular affordance using virtual human models. Therefore, our approach does not require to observe real humans in the scene. The affordance-map and object-human-robot relationship are then used to infer the object search strategy. We tested our algorithm using a mobile robot that actively searched for the object "computer monitors" in an office environment with promising results.