25,735 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
Appearance-based localization for mobile robots using digital zoom and visual compass
This paper describes a localization system for mobile robots moving in dynamic indoor environments, which uses probabilistic integration of visual appearance and odometry information. The approach is based on a novel image matching algorithm for appearance-based place recognition that integrates digital zooming, to extend the area of application, and a visual compass. Ambiguous information used for recognizing places is resolved with multiple hypothesis tracking and a selection procedure inspired by Markov localization. This enables the system to deal with perceptual aliasing or absence of reliable sensor data. It has been implemented on a robot operating in an office scenario and the robustness of the approach demonstrated experimentally
Mapping, Localization and Path Planning for Image-based Navigation using Visual Features and Map
Building on progress in feature representations for image retrieval,
image-based localization has seen a surge of research interest. Image-based
localization has the advantage of being inexpensive and efficient, often
avoiding the use of 3D metric maps altogether. That said, the need to maintain
a large number of reference images as an effective support of localization in a
scene, nonetheless calls for them to be organized in a map structure of some
kind.
The problem of localization often arises as part of a navigation process. We
are, therefore, interested in summarizing the reference images as a set of
landmarks, which meet the requirements for image-based navigation. A
contribution of this paper is to formulate such a set of requirements for the
two sub-tasks involved: map construction and self-localization. These
requirements are then exploited for compact map representation and accurate
self-localization, using the framework of a network flow problem. During this
process, we formulate the map construction and self-localization problems as
convex quadratic and second-order cone programs, respectively. We evaluate our
methods on publicly available indoor and outdoor datasets, where they
outperform existing methods significantly.Comment: CVPR 2019, for implementation see https://github.com/janinethom
- …