3,077 research outputs found
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Topomap: Topological Mapping and Navigation Based on Visual SLAM Maps
Visual robot navigation within large-scale, semi-structured environments
deals with various challenges such as computation intensive path planning
algorithms or insufficient knowledge about traversable spaces. Moreover, many
state-of-the-art navigation approaches only operate locally instead of gaining
a more conceptual understanding of the planning objective. This limits the
complexity of tasks a robot can accomplish and makes it harder to deal with
uncertainties that are present in the context of real-time robotics
applications. In this work, we present Topomap, a framework which simplifies
the navigation task by providing a map to the robot which is tailored for path
planning use. This novel approach transforms a sparse feature-based map from a
visual Simultaneous Localization And Mapping (SLAM) system into a
three-dimensional topological map. This is done in two steps. First, we extract
occupancy information directly from the noisy sparse point cloud. Then, we
create a set of convex free-space clusters, which are the vertices of the
topological map. We show that this representation improves the efficiency of
global planning, and we provide a complete derivation of our algorithm.
Planning experiments on real world datasets demonstrate that we achieve similar
performance as RRT* with significantly lower computation times and storage
requirements. Finally, we test our algorithm on a mobile robotic platform to
prove its advantages.Comment: 8 page
Knowledge Representation for Robots through Human-Robot Interaction
The representation of the knowledge needed by a robot to perform complex
tasks is restricted by the limitations of perception. One possible way of
overcoming this situation and designing "knowledgeable" robots is to rely on
the interaction with the user. We propose a multi-modal interaction framework
that allows to effectively acquire knowledge about the environment where the
robot operates. In particular, in this paper we present a rich representation
framework that can be automatically built from the metric map annotated with
the indications provided by the user. Such a representation, allows then the
robot to ground complex referential expressions for motion commands and to
devise topological navigation plans to achieve the target locations.Comment: Knowledge Representation and Reasoning in Robotics Workshop at ICLP
201
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
Learning Models for Following Natural Language Directions in Unknown Environments
Natural language offers an intuitive and flexible means for humans to
communicate with the robots that we will increasingly work alongside in our
homes and workplaces. Recent advancements have given rise to robots that are
able to interpret natural language manipulation and navigation commands, but
these methods require a prior map of the robot's environment. In this paper, we
propose a novel learning framework that enables robots to successfully follow
natural language route directions without any previous knowledge of the
environment. The algorithm utilizes spatial and semantic information that the
human conveys through the command to learn a distribution over the metric and
semantic properties of spatially extended environments. Our method uses this
distribution in place of the latent world model and interprets the natural
language instruction as a distribution over the intended behavior. A novel
belief space planner reasons directly over the map and behavior distributions
to solve for a policy using imitation learning. We evaluate our framework on a
voice-commandable wheelchair. The results demonstrate that by learning and
performing inference over a latent environment model, the algorithm is able to
successfully follow natural language route directions within novel, extended
environments.Comment: ICRA 201
Monte Carlo Localization in Hand-Drawn Maps
Robot localization is a one of the most important problems in robotics. Most
of the existing approaches assume that the map of the environment is available
beforehand and focus on accurate metrical localization. In this paper, we
address the localization problem when the map of the environment is not present
beforehand, and the robot relies on a hand-drawn map from a non-expert user. We
addressed this problem by expressing the robot pose in the pixel coordinate and
simultaneously estimate a local deformation of the hand-drawn map. Experiments
show that we are able to localize the robot in the correct room with a
robustness up to 80
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