19,619 research outputs found
Pop-up SLAM: Semantic Monocular Plane SLAM for Low-texture Environments
Existing simultaneous localization and mapping (SLAM) algorithms are not
robust in challenging low-texture environments because there are only few
salient features. The resulting sparse or semi-dense map also conveys little
information for motion planning. Though some work utilize plane or scene layout
for dense map regularization, they require decent state estimation from other
sources. In this paper, we propose real-time monocular plane SLAM to
demonstrate that scene understanding could improve both state estimation and
dense mapping especially in low-texture environments. The plane measurements
come from a pop-up 3D plane model applied to each single image. We also combine
planes with point based SLAM to improve robustness. On a public TUM dataset,
our algorithm generates a dense semantic 3D model with pixel depth error of 6.2
cm while existing SLAM algorithms fail. On a 60 m long dataset with loops, our
method creates a much better 3D model with state estimation error of 0.67%.Comment: International Conference on Intelligent Robots and Systems (IROS)
201
Measurement of NO2 indoor and outdoor concentrations in selected public schools of Lahore using passive sampler
Higher levels of NO2 are a danger to human health especially for children. A seven day study was carried to find out the
ambient concentrations of NO2in 27 schools of Lahore with the help of passive samplers. In each school three sites were
selected, viz: laboratory, corridor and outdoors. After 7 days exposure the tubes were subjected to spectrophotometric
analysis. Results showed that the maximum values measured in laboratory, outdoor and corridors were 376µg/m3 ,
222µg/m3 and 77µg/m3 . Minimum values for laboratory, outdoor and corridors were 10µg/m3 , 20µg/m3 and 8µg/m3 .
Factors affecting these values were laboratory activities and proximity to main roads. These values were significantly
higher than the standard values defined by EPA. Therefore children in schools were at risk of developing health
complications
Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme
Behavior planning and decision-making are some of the biggest challenges for
highly automated systems. A fully automated vehicle (AV) is confronted with
numerous tactical and strategical choices. Most state-of-the-art AV platforms
implement tactical and strategical behavior generation using finite state
machines. However, these usually result in poor explainability, maintainability
and scalability. Research in robotics has raised many architectures to mitigate
these problems, most interestingly behavior-based systems and hybrid
derivatives. Inspired by these approaches, we propose a hierarchical
behavior-based architecture for tactical and strategical behavior generation in
automated driving. It is a generalizing and scalable decision-making framework,
utilizing modular behavior blocks to compose more complex behaviors in a
bottom-up approach. The system is capable of combining a variety of scenario-
and methodology-specific solutions, like POMDPs, RRT* or learning-based
behavior, into one understandable and traceable architecture. We extend the
hierarchical behavior-based arbitration concept to address scenarios where
multiple behavior options are applicable but have no clear priority against
each other. Then, we formulate the behavior generation stack for automated
driving in urban and highway environments, incorporating parking and emergency
behaviors as well. Finally, we illustrate our design in an explanatory
evaluation
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
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