Abstract
Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics.
Highlights
The evolution of mobile technologies and sensors has increased the complexity of autonomous robot behaviors in many scenarios, including Simultaneous Localization and Mapping (SLAM) applications [1,2]
SLAM with the Pepper robot and the Gmapping algorithm [43]; this map was built based on information from the laser_scan sensors of Robot “A”; (iii) Figure 11c presents the map recovered from the ontology instance, developed by the Robot “B”, showing the result of the Semantic Data Querying phase presented on the Rviz visualizer; (iv) Figure 11d shows the 3D map constructed by the same Robot “A” and in the same scenario, but with the octomap mapping algorithm [44], which uses the point cloud generated by the depth sensor of Robot “A”; and (v) Figure 11e, presents the recovered map by the Robot “B” from OntoSLAM
From the simulated scenarios with Robot Operating System (ROS) and Gazebo, it was demonstrated that no information is lost while transforming the information to the ontology instance and querying it afterwards. This achieves several benefits, such as: (i) the map can be partially constructed at certain moment, the partial map can be stored in the ontology, and continue the map construction in another later time; (ii) the map can be constructed by two different robots, at different times since the ontology takes over as the moderator; and (iii) a complete map can be recovered by other robots to do not repeat the SLAM process, and used it for other purposes. In this work it is presented OntoSLAM, an ontology for modeling all aspects related to SLAM knowledge, in contrast of existing ontologies that only represent partially that knowledge, mainly focusing on the result of the SLAM process and neglecting the dynamic nature of the SLAM process
Summary
The evolution of mobile technologies and sensors has increased the complexity of autonomous robot behaviors in many scenarios, including Simultaneous Localization and Mapping (SLAM) applications [1,2]. Some studies have formulated ontologies to partially model the information related to some aspects of SLAM, as shown in the studies presented in [6,7], that propose a categorization of the knowledge domain of SLAM and compare state-of-the-art SLAM ontologies. To develop a complete ontology, it is necessary to consider the outcome of SLAM applications, and to examine the inherent characteristics of the SLAM dynamics, such as uncertainty To overcome these limitations, in this work it is proposed OntoSLAM, an ontology that represents all knowledge related to autonomous robots and the SLAM problem considered to be a continuous process with the presence of uncertainty in robots and landmarks positions.
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