Incorporating extrinsic object properties in robotic semantic mapping

Most robot semantic mapping methods only consider the intrinsic properties of landmarks and objects inside a scene, by detecting them with their appearances, and some other methods include extrinsic properties with manually designed object relations. In this work, we use relational operators to capture the extrinsic property values, and adopt conditional random field to integrate intrinsic and extrinsic property values into semantic mapping. We compare our approach with three types of semantic maps, and show that our approach allows the robot to find designated objects more accurately.