Simultaneous localization and mapping
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The world's premier EDA and semiconductor design conference and exhibition. DAC features over 60 sessions on design methodologies and EDA tool developments, keynotes, panels, plus the NEW User Track presentations. A diverse worldwide community representing more than 1,000 organizations attends each year, from system designers and architects, logic and circuit designers, validation engineers, CAD managers, senior managers and executives to researchers and academicians from leading universities.
The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
The Conference focuses on all aspects of instrumentation and measurement science andtechnology research development and applications. The list of program topics includes but isnot limited to: Measurement Science & Education, Measurement Systems, Measurement DataAcquisition, Measurements of Physical Quantities, and Measurement Applications.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
The IEEE Transactions on Automation Sciences and Engineering (T-ASE) publishes fundamental papers on Automation, emphasizing scientific results that advance efficiency, quality, productivity, and reliability. T-ASE encourages interdisciplinary approaches from computer science, control systems, electrical engineering, mathematics, mechanical engineering, operations research, and other fields. We welcome results relevant to industries such as agriculture, biotechnology, healthcare, home automation, maintenance, manufacturing, pharmaceuticals, retail, ...
After nine years of publication, DS Online will be moving into a new phase as part of Computing Now (http://computingnow.computer.org), a new website providing the front end to all of the Computer Society's magazines. As such, DS Online will no longer be publishing standalone peer-reviewed articles.
Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...
It is expected that GRS Letters will apply to a wide range of remote sensing activities looking to publish shorter, high-impact papers. Topics covered will remain within the IEEE Geoscience and Remote Sensing Societys field of interest: the theory, concepts, and techniques of science and engineering as they apply to the sensing of the earth, oceans, atmosphere, and space; and ...
ROBOTIK 2012; 7th German Conference on Robotics, 2012
A very important prerequisite for mobile robots to navigate autonomously is their ability to build a map while exploring unknown areas. In this paper we present an algorithm for simultaneous localization and mapping for indoor environments based on the Kinect sensor. The algorithm is capable of building a 3D voxel map including color information and performing localization with scan matching. ...
Proceedings of the IEEE, 2018
Visual understanding of 3-D environments in real time, at low power, is a huge computational challenge. Often referred to as simultaneous localization and mapping (SLAM), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, and virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems ...
IEEE Transactions on Robotics, 2016
We propose a scalable algorithm to take advantage of the separable structure of simultaneous localization and mapping (SLAM). Separability is an overlooked structure of SLAM that distinguishes it from a generic nonlinear least-squares problem. The standard relative-pose and relative-position measurement models in SLAM are affine with respect to robot and features' positions. Therefore, given an estimate for robot orientation, the ...
Computers in Cardiology 1998. Vol. 25 (Cat. No.98CH36292), 1998
A method based on computed-tomographic reconstruction was developed to determine three-dimensional locations of electrodes on a concentric pair of a cylindrical probe and a basket-shaped catheter deployed in the intact canine left ventricle. The reconstructed geometric model was validated by assessing the quality of the solution to the forward problem relating endocardial potentials to probe potentials.
2010 International Conference On Computer Design and Applications, 2010
Local scale-invariant features are used as natural landmarks in unstructured and unmodified environment. As autonomous robots, possessing visual acquisition capability is very crucial to explore unknown environments reliably, SIFT (Scale Invariant Feature Transform) key points are powerful in detecting objects under various imaging conditions, robot can use the recognized object as landmarks to navigate and localize itself. This paper presents ...
Localization Services for Online Common Operational Picture and Situation Awareness
Mapping Human to Robot Motion with Functional Anthropomorphism for Teleoperation and Telemanipulation with Robot Arm Hand Systems
A Wideband Single-PLL RF Receiver for Simultaneous Multi-Band and Multi-Channel Digital Car Radio Reception: RFIC Industry Showcase
Imaging Human Brain Function with Simultaneous EEG-fMRI - IEEE Brain Workshop
3D Body-Mapping for Severely Burned Patients - Julia Loegering - IEEE EMBS at NIH, 2019
Comparing Modern Multiport VNA vs. Conventional Switch-based VNA: MicroApps 2015 - Keysight Technologies
Road-Mapping Session with Deepa Prahalad at Internet Inclusion: Advancing Solutions, Delhi, 2016
Q&A with Jack Gallant: IEEE Brain Podcast, Episode 11
Robotics History: Narratives and Networks Oral Histories: Gary Bradsky
Clinton Andrews leads the ETAP Forum Vote on the Top Four IoT Issues
Stephen Weinstein accepts the IEEE Richard M. Emberson Award - Honors Ceremony 2016
ISEC 2013 Special Gordon Donaldson Session: Remembering Gordon Donaldson - 5 of 7 - SQUID Instrumentation for Early Cancer Diagnostics
LPIRC: A Facebook Approach to Benchmarking ML Workload
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
The Autonomous City Explorer (ACE) Project--Mobile Robot Navigation in Highly Populated Urban Environments
NEREID: Systems Design & Heterogeneous Integration: Danilo Demarchi at INC 2019
IEEE Magnetics Distinguished Lecture - Alison B. Flatau
Uncovering the Neural Code of Learning Control - Jennie Si - WCCI 2012 invited lecture
Neato Robotics' Roomba Competitor
Accurate and precise navigation for aerial platforms is not possible in Global Positioning System denied environment. Several methods to aid Inertial Navigation System have been proposed and used to cope with this problem. Simultaneous localization and mapping is one of the most popular among these. The main idea behind visual simultaneous localization and mapping is to estimate errors which exist on navigation outputs and landmark position predictions by using position differences of landmarks, which their position on the ground are unknown previously, in consecutive frames. The fundamental problem occurs in visual simultaneous localization and mapping is depth ambiguity due to 2-dimensional imaging. In this study, visual simultaneous localization and mapping where prediction of depth information is done by aid of terrain elevation information is explained.
SLAM, Simultaneous Localization and Mapping
This course covers the general area of Simultaneous Localization and Mapping (SLAM). Initially the problems of localization, mapping, and SLAM are introduced from a methodological point of view. Different methods for representation of uncertainty will be introduced including their ability to handle single and multi-mode uncertainty representations. A number of example applications are discussed.
Itpsilas preferred that a hierarchical map is adopted to describe large environments, as seen in the literatures about the hierarchical simultaneous localization and mapping (HSLAM). With an automaton map at the upper layer and a set of feature-based maps at the lower layer, both macro and micro localization are realized. A novel exploration strategy which guides and supervises the mapping progress based on supervisory control of discrete event systems is proposed. Itpsilas embodied in the system and implemented as a supervisor which permits some desired actions to occur and prohibits undesired actions.
This paper proposed robot partner by using simultaneous localization and mapping based on computational intelligence. The target of the paper is the application of map building and map's noise reduction method for mobile robot in living space based only on distance data. First, we proposed method of self-location update. In this paper, Robot partner could updates self-location by using the steady-state genetic algorithm. Next, we propose map building method based on a topological map based on growing topological neural network. Then we propose noise reduction for the mapping. Finally, we discuss the effectiveness of the proposed methods. Due to the results, we can confirm the usefulness of the proposed method.
In view of the problems of low precision of localization and detection method based on traditional navigation with non-Gauss and non-linear model in underwater, a feature map generation algorithm based on the simultaneous localization and mapping (SLAM) was proposed. Image processing and feature extraction method based on SLAM were presented in this paper, and characteristic of shipwreck were matched and extracted respectively. The experimental results proved that the new algorithm could achieve higher correlation degree of feature matching and complete the feature map reconstruction. Finally, by building the system state model and observation model based on SLAM, and using Square-Root Cubature Kalman filter based on Spherical Simplex (SS-SRCKF) algorithm to estimate and analysis the state of carrier and the characteristics, the experimental results showed that the new filtering algorithm could realize high precision navigation with small error.
This paper presents object scanning for landmarks detection based on multiple scanning of a non-stationary and discrete sensor for the autonomous vehicle in unknown environment. To preserve a complete mapping and localization techniques, a massive, expensive and complicated computation as well as algorithms have been applied by numerous researchers. On top of that, the utilized sensors are often costly to provide an accurate landmarks or obstacles observation and detection. Therefore, in this work, a mapping technique based on Simultaneous Localization and Mapping (SLAM) in the unknown environment for the autonomous vehicle platform is designed by implementing a low cost rotational non-stationary discrete sensor to visualize and differentiate the objects or obstacles that is useful for the unmanned vehicle to find the eligible pathways during the navigation. This sensor is mounted on the servo motor so that it can rotate while measuring the distance of the detected object, in x, y and z-planes to perform the multiple scanning operation. The scanned data is collected and then plotted in three-dimensional (3D) graphical image representation by using MATLAB to construct the image of the detected obstacle or landmark in this unknown environment. It is found that, the infrared sensor is more suitable to define the shape of the detected object or landmark than to determine the size of it.
This paper addresses the problem of mobile device localization in wireless sensor networks. The mobile is assumed to receive the signals transmitted byWiFi access points. The localization procedure is performed online (i.e. using the observations acquired by the mobile) and relies on the estimation of the propagation maps of the signal associated with each access point. This intermediate estimation step uses a new online Expectation Maximization based algorithm and Sequential Monte Carlo methods.
This article applies scaled unscented transformation to the simultaneous localization and map building algorithm in two different ways. One is for the entire vehicle-map states by replacing EKF with the unscented Kalman filtering (UKF) to carry on the state estimation; the other is for the vehicle states by using the EKF both in the prediction of the map feature and the update of the complete state vector. The plentiful Monter-Carlo simulations were carried out to evaluate the algorithms' performance. The simulation results indicate that both two methods can reduce the EKF linearization error effectively, and the second method is more efficient in computation.
In order to make the simultaneous localization and mapping (SLAM) algorithm universal, a novel hierarchical approach, which realizes both macro and micro localization and mapping for a kind of large environments is proposed. The map is a two-layer architecture which the upper layer is an automaton and the lower layer is a set of feature-based maps. The exploring strategy for unknown space was embodied in the system. Itpsilas described by a supervisor which permits some desired actions to occur and prohibits undesired actions. The approach proposed here has the potential ability of localizing the robot in maze like environments.
Simultaneous Localization and Mapping is an important technology which help a mobile robot to determine its location and build the environment map. Recently, the RGBD sensor is widely used in the robot, research on RGBD-SLAM becomes a hot topic. In order to calculate the movement parameters of robot, feature matching is adopted to register the two adjacent RGBD images in the video stream. This paper proposed an improved feature matching method for RGBD-SLAM. The experiment results show that, compared with the traditional SIFT feature matching methods for RGBD-SLAM, the performance of the proposed method is improved significantly.
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