22,081 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
Fast, Autonomous Flight in GPS-Denied and Cluttered Environments
One of the most challenging tasks for a flying robot is to autonomously
navigate between target locations quickly and reliably while avoiding obstacles
in its path, and with little to no a-priori knowledge of the operating
environment. This challenge is addressed in the present paper. We describe the
system design and software architecture of our proposed solution, and showcase
how all the distinct components can be integrated to enable smooth robot
operation. We provide critical insight on hardware and software component
selection and development, and present results from extensive experimental
testing in real-world warehouse environments. Experimental testing reveals that
our proposed solution can deliver fast and robust aerial robot autonomous
navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field
Robotic
Marker based Thermal-Inertial Localization for Aerial Robots in Obscurant Filled Environments
For robotic inspection tasks in known environments fiducial markers provide a
reliable and low-cost solution for robot localization. However, detection of
such markers relies on the quality of RGB camera data, which degrades
significantly in the presence of visual obscurants such as fog and smoke. The
ability to navigate known environments in the presence of obscurants can be
critical for inspection tasks especially, in the aftermath of a disaster.
Addressing such a scenario, this work proposes a method for the design of
fiducial markers to be used with thermal cameras for the pose estimation of
aerial robots. Our low cost markers are designed to work in the long wave
infrared spectrum, which is not affected by the presence of obscurants, and can
be affixed to any object that has measurable temperature difference with
respect to its surroundings. Furthermore, the estimated pose from the fiducial
markers is fused with inertial measurements in an extended Kalman filter to
remove high frequency noise and error present in the fiducial pose estimates.
The proposed markers and the pose estimation method are experimentally
evaluated in an obscurant filled environment using an aerial robot carrying a
thermal camera.Comment: 10 pages, 5 figures, Published in International Symposium on Visual
Computing 201
Multi-View Picking: Next-best-view Reaching for Improved Grasping in Clutter
Camera viewpoint selection is an important aspect of visual grasp detection,
especially in clutter where many occlusions are present. Where other approaches
use a static camera position or fixed data collection routines, our Multi-View
Picking (MVP) controller uses an active perception approach to choose
informative viewpoints based directly on a distribution of grasp pose estimates
in real time, reducing uncertainty in the grasp poses caused by clutter and
occlusions. In trials of grasping 20 objects from clutter, our MVP controller
achieves 80% grasp success, outperforming a single-viewpoint grasp detector by
12%. We also show that our approach is both more accurate and more efficient
than approaches which consider multiple fixed viewpoints.Comment: ICRA 2019 Video: https://youtu.be/Vn3vSPKlaEk Code:
https://github.com/dougsm/mvp_gras
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Localization from semantic observations via the matrix permanent
Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization
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