8,305 research outputs found
Interactive semantic mapping: Experimental evaluation
Robots that are launched in the consumer market need to provide more effective human robot interaction, and, in particular, spoken language interfaces. However, in order to support the execution of high level commands as they are specified in natural language, a semantic map is required. Such a map is a representation that enables the robot to ground the commands into the actual places and objects located in the environment. In this paper, we present the experimental evaluation of a system specifically designed to build semantically rich maps, through the interaction with the user. The results of the experiments not only provide the basis for a discussion of the features of the proposed approach, but also highlight the manifold issues that arise in the evaluation of semantic mapping
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Semantic grid map building
Conventional Occupancy Grid (OG) map which contains occupied and unoccupied cells can be enhanced by incorporating semantic labels of places to build semantic grid map. Map with semantic information is more understandable to humans and hence can be used for efficient communication, leading to effective human robot interactions. This paper proposes a new approach that enables a robot to explore an indoor environment to build an occupancy grid map and then perform semantic labeling to generate a semantic grid map. Geometrical information is obtained by classifying the places into three different semantic classes based on data collected by a 2D laser range finder. Classification is achieved by implementing logistic regression as a multi-class classifier, and the results are combined in a probabilistic framework. Labeling accuracy is further improved by topological correction on robot position map which is an intermediate product, and also by outlier removal process on semantic grid map. Simulation on data collected in a university environment shows appealing results
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
No abstract available
GUARDIANS final report
Emergencies in industrial warehouses are a major concern for firefghters. The large dimensions together with the development of dense smoke that drastically reduces visibility, represent major challenges. The Guardians robot swarm is designed to assist fire fighters in searching a
large warehouse. In this report we discuss the technology developed for a swarm of robots searching and assisting fire fighters. We explain the swarming algorithms which provide the functionality by which the robots react to and follow humans while no communication is required. Next we
discuss the wireless communication system, which is a so-called mobile ad-hoc network. The communication network provides also one of the means to locate the robots and humans. Thus the robot swarm is able to locate itself and provide guidance information to the humans. Together with
the re ghters we explored how the robot swarm should feed information back to the human fire fighter. We have designed and experimented with interfaces for presenting swarm based information to human beings
Enabling Topological Planning with Monocular Vision
Topological strategies for navigation meaningfully reduce the space of
possible actions available to a robot, allowing use of heuristic priors or
learning to enable computationally efficient, intelligent planning. The
challenges in estimating structure with monocular SLAM in low texture or highly
cluttered environments have precluded its use for topological planning in the
past. We propose a robust sparse map representation that can be built with
monocular vision and overcomes these shortcomings. Using a learned sensor, we
estimate high-level structure of an environment from streaming images by
detecting sparse vertices (e.g., boundaries of walls) and reasoning about the
structure between them. We also estimate the known free space in our map, a
necessary feature for planning through previously unknown environments. We show
that our mapping technique can be used on real data and is sufficient for
planning and exploration in simulated multi-agent search and learned subgoal
planning applications.Comment: 7 pages (6 for content + 1 for references), 5 figures. Accepted to
the 2020 IEEE International Conference on Robotics and Automatio
Building a grid-semantic map for the navigation of service robots through human–robot interaction
AbstractThis paper presents an interactive approach to the construction of a grid-semantic map for the navigation of service robots in an indoor environment. It is based on the Robot Operating System (ROS) framework and contains four modules, namely Interactive Module, Control Module, Navigation Module and Mapping Module. Three challenging issues have been focused during its development: (i) how human voice and robot visual information could be effectively deployed in the mapping and navigation process; (ii) how semantic names could combine with coordinate data in an online Grid-Semantic map; and (iii) how a localization–evaluate–relocalization method could be used in global localization based on modified maximum particle weight of the particle swarm. A number of experiments are carried out in both simulated and real environments such as corridors and offices to verify its feasibility and performance
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