RoboEarth Semantic Mapping: A Cloud Enabled Knowledge-Based Approach

Abstract

Abstract-The vision of the RoboEarth project is to design a knowledge-based system to provide web and cloud services that can transform a simple robot into an intelligent one. In this work we describe the RoboEarth semantic mapping system. The semantic map is composed of (1) an ontology to code the concepts and relations in maps and objects, and (2) a SLAM map providing the scene geometry and the object locations with respect to the robot. We propose to ground the terminological knowledge in the robot perceptions by means of the SLAM map of objects. RoboEarth boosts mapping by providing: (1) a subdatabase of object models relevant for the task at hand, obtained by semantic reasoning, which improves recognition by reducing computation and the false positive rate; (2) the sharing of semantic maps between robots, and (3) software as a service to externalize in the cloud the more intensive mapping computations, while meeting the mandatory hard real time constraints of the robot.To demonstrate the RoboEarth cloud mapping system, we investigate two action recipes that embody semantic map building in a simple mobile robot. The first recipe enables semantic map building for a novel environment while exploiting available prior information about the environment. The second recipe searches for a novel object, with the efficiency boosted thanks to the reasoning on a semantically annotated map. Our experimental results demonstrate that by using RoboEarth cloud services, a simple robot can reliably and efficiently build the semantic maps needed to perform its quotidian tasks. In addition, we show the synergetic relation of the SLAM map of objects that grounds the terminological knowledge coded in the ontology.Note to Practitioners-RoboEarth is a cloud-based knowledge base for robots that transforms a simple robot into an intelligent one thanks to the web services provided. As mapping is a mandatory element on most of the robot systems, we focus on the RoboEarth semantic mapping for robot systems, showing the benefits of the combination of SLAM (Simultaneous Localization And Map building), and knowledge-based reasoning. We show the qualities of our system by means of two experiments: (1) building a map of a novel environment boosted by prior information and (2) efficient searching for a novel object thanks to the knowledgebased reasoning techniques. We can conclude that RoboEarth enables the execution of the proposed methods as web and cloud services that enable advanced perception in a simple robot.

Cloud Robotics: Current Status and Open Issues

“…In order to achieve robot navigation, L. Riazuelo et al [22] used a cloud platform for SLAM, achieving very good results. In addition, the combination of cloud computing and robotics in video, image analysis, data mining and other fields is an important direction of the future development of cloud robotics.…”

“…In 2009, the European Union project RoboEarth [6,7], led by Holland's Eindhoven University, and resulted in the development of a World Wide Web equivalent for robots. RoboEarth can be regarded as a web-based large database, the implementation of which allows robots to share information learned from each other, and finally update the database to achieve a virtuous closed loop.…”

Extracting Semantic Information from Visual Data: A Survey

“…Tian et al also addressed this problem, since doors are generally considered as distinguishable landmarks between rooms in indoor environments [11]. Wu et al [12] and Neves dos Santos et al [13] employed 2D visual data to tackle the problem of place recognition in semantic mapping, and object recognition with monocular cameras was presented by Civera et al [14] and Riazuelo et al [15]. …”

“…Riazuelo et al initially extracted speeded up robust features (SURF) from each image and stored them for later object recognition in the RoboEarth database, which is a knowledge-based system providing web and cloud services [15]. SURF has claimed to be several times faster than SIFT, and the accuracy still remains relatively acceptable.…”

Semantic region estimation of assistant robot for the elderly long-term operation in indoor environment

“…However, relations would be extremely complicated when too many chosen objects are involved. Luis et al [19] proposed a RoboEarth semantic mapping system, which put relations among chosen objects on the cloud services, to solve complicated relations. Like place recognition, these approaches rely on statically comparing with patterns in the provided database, lacking dynamic mechanism.…”