PRISM: Pose Registration for Integrated Semantic Mapping

Many robotics applications involve navigating to positions specified in terms of their semantic significance. A robot operating in a hotel may need to deliver room service to a named room. In a hospital, it may need to deliver medication to a patient's room. The Building-Wide Intelligence Project at UT Austin has been developing a fleet of autonomous mobile robots, called BWIBots, which perform tasks in the computer science department. Tasks include guiding a person, delivering a message, or bringing an object to a location such as an office, lecture hall, or classroom. The process of constructing a map that a robot can use for navigation has been simplified by modern SLAM algorithms. The attachment of semantics to map data, however, remains a tedious manual process of labeling locations in otherwise automatically generated maps. This paper introduces a system called PRISM to automate a step in this process by enabling a robot to localize door signs - a semantic markup intended to aid the human occupants of a building - and to annotate these locations in its map.

I. Introduction

Emerging applications in robotics involve systems in which robots navigate to static, known locations in order to perform their tasking. In environments like hotels, hospitals, and university buildings these locations are frequently understood through a semantic label such as the room number of a classroom or office. Whereas robots generally use Simultaneous Localization and Mapping (SLAM) algorithms for navigation, the poses associated with such semantics are generally labeled through manual processes after the SLAM map has been constructed. Humans, on the other hand, leverage semantics provided to them by the building itself in the form of signage such as room number placards.