8,074 research outputs found
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
Toward autonomous exploration in confined underwater environments
Author Posting. © The Author(s), 2015. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 33 (2016): 994-1012, doi:10.1002/rob.21640.In this field note we detail the operations and discuss the results of an experiment conducted
in the unstructured environment of an underwater cave complex, using an autonomous underwater vehicle (AUV). For this experiment the AUV was equipped with two acoustic
sonar to simultaneously map the caves’ horizontal and vertical surfaces. Although the
caves’ spatial complexity required AUV guidance by a diver, this field deployment successfully demonstrates a scan matching algorithm in a simultaneous localization and mapping (SLAM) framework that significantly reduces and bounds the localization error for fully
autonomous navigation. These methods are generalizable for AUV exploration in confined
underwater environments where surfacing or pre-deployment of localization equipment are
not feasible and may provide a useful step toward AUV utilization as a response tool in
confined underwater disaster areas.This research work was partially sponsored by the EU FP7-Projects: Tecniospring-
Marie Curie (TECSPR13-1-0052), MORPH (FP7-ICT-2011-7-288704), Eurofleets2 (FP7-INF-2012-312762),
and the National Science Foundation (OCE-0955674)
Active Simultaneous Localization and Mapping in Unstructured Environment with a Quadrotor
An agent performing Simultaneous Localization and Mapping (SLAM) constructs a map of the environment while estimating its location at the same time. SLAM algorithms primarily focus on finding the best estimate of the location of the agent, and by extension, that of the landmarks in the environment, given observations from sensors. These algorithms do not typically address how an agent should explore an unknown environment to build a map efficiently. This ability for active exploration is important for autonomous robots to work in unknown, unstructured environments such as forests or caves.
This paper proposes an active SLAM system that allows an agent to explore its surroundings, using visual-inertial data from an RGBD camera. We formalize this problem as taking actions that maximize the amount of information obtained from the scene. At each time step, a utility function that computes the incremental information gain is used to take actions. We conduct experiments using an Intel RealSense camera mounted on a custom-built quadrotor and show that we can explore indoor environments (while restricting the actions to choosing new viewpoints in SO(3) for practical reasons)
Robots for Exploration, Digital Preservation and Visualization of Archeological Sites
Monitoring and conservation of archaeological sites
are important activities necessary to prevent damage or to
perform restoration on cultural heritage. Standard techniques,
like mapping and digitizing, are typically used to document the
status of such sites. While these task are normally accomplished
manually by humans, this is not possible when dealing with
hard-to-access areas. For example, due to the possibility of
structural collapses, underground tunnels like catacombs are
considered highly unstable environments. Moreover, they are full
of radioactive gas radon that limits the presence of people only
for few minutes. The progress recently made in the artificial
intelligence and robotics field opened new possibilities for mobile
robots to be used in locations where humans are not allowed
to enter. The ROVINA project aims at developing autonomous
mobile robots to make faster, cheaper and safer the monitoring of
archaeological sites. ROVINA will be evaluated on the catacombs
of Priscilla (in Rome) and S. Gennaro (in Naples)
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