Simultaneous localization and mapping

Simultaneous localization and mapping (SLAM) is a technique used by robots and autonomous vehicles to build up a map within an unknown environment (without a priori knowledge), or to update a map within a known environment (with a priori knowledge from a given map), while at the same time keeping track of their current location. (Wikipedia.org)






Conferences related to Simultaneous localization and mapping

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OCEANS 2020 - SINGAPORE

An OCEANS conference is a major forum for scientists, engineers, and end-users throughout the world to present and discuss the latest research results, ideas, developments, and applications in all areas of oceanic science and engineering. Each conference has a specific theme chosen by the conference technical program committee. All papers presented at the conference are subsequently archived in the IEEE Xplore online database. The OCEANS conference comprises a scientific program with oral and poster presentations, and a state of the art exhibition in the field of ocean engineering and marine technology. In addition, each conference can have tutorials, workshops, panel discussions, technical tours, awards ceremonies, receptions, and other professional and social activities.

  • OCEANS 2005 - EUROPE

  • OCEANS 2006 - ASIA PACIFIC

  • OCEANS 2007 - EUROPE

    The theme 'Marine Challenges: Coastline to Deep Sea' focuses on the significant challenges, from the shallowest waters around our coasts to the deepest subsea trenches, that face marine, subsea and oceanic engineers in their drive to understand the complexities of the world's oceans.

  • OCEANS 2008 - MTS/IEEE Kobe Techno-Ocean

  • OCEANS 2009 - EUROPE

  • OCEANS 2010 IEEE - Sydney

  • OCEANS 2011 - SPAIN

    All Oceans related technologies.

  • OCEANS 2012 - YEOSU

    The OCEANS conferences covers four days with tutorials, exhibits and three days of parallel tracks that address all aspects of oceanic engineering.

  • OCEANS 2013 - NORWAY

    Ocean related technologies. Program includes tutorials, three days of technical papers and a concurrent exhibition. Student poster competition.

  • OCEANS 2014 - TAIPEI

    The OCEANS conference covers all aspects of ocean engineering from physics aspects through development and operation of undersea vehicles and equipment.

  • OCEANS 2015 - Genova

    The Marine Technology Society and the Oceanic Engineering Society of IEEE cosponsor a joint annual conference and exposition on ocean science, engineering and policy. The OCEANS conference covers four days. One day for tutorials and three for approx. 450 technical papers and 50-200 exhibits.

  • OCEANS 2016 - Shanghai

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorial on ocean technology, workshops and town hall meetings on policy and governmental process.

  • OCEANS 2017 - Aberdeen

    Papers on ocean technology, exhibits from ocean equipment and service suppliers, student posters and student poster competition, tutorials on ocean technology, workshops and town hall meetings on policy and governmental process.

  • 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO)

    The conference scope is to provide a thematic umbrella for researchers working in OCEAN engineering and related fields across the world to discuss the problems and potential long term solutions that concernnot only the oceans in Asian pacific region, but the world ocean in general.

  • OCEANS 2019 - Marseille

    Research, Development, and Operations pertaining to the Oceans


2020 IEEE International Conference on Robotics and Automation (ICRA)

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.


2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

The scope of the 2020 IEEE/ASME AIM includes the following topics: Actuators, Automotive Systems, Bioengineering, Data Storage Systems, Electronic Packaging, Fault Diagnosis, Human-Machine Interfaces, Industry Applications, Information Technology, Intelligent Systems, Machine Vision, Manufacturing, Micro-Electro-Mechanical Systems, Micro/Nano Technology, Modeling and Design, System Identification and Adaptive Control, Motion Control, Vibration and Noise Control, Neural and Fuzzy Control, Opto-Electronic Systems, Optomechatronics, Prototyping, Real-Time and Hardware-in-the-Loop Simulation, Robotics, Sensors, System Integration, Transportation Systems, Smart Materials and Structures, Energy Harvesting and other frontier fields.


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.

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2018 IEEE/CVF 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.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.


2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

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.



Periodicals related to Simultaneous localization and mapping

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Automatic Control, IEEE Transactions on

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 ...


Automation Science and Engineering, IEEE Transactions on

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, ...


Distributed Systems Online, IEEE

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.


Fuzzy Systems, IEEE Transactions on

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 ...


Geoscience and Remote Sensing Letters, IEEE

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 ...



Most published Xplore authors for Simultaneous localization and mapping

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Xplore Articles related to Simultaneous localization and mapping

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Simultaneous Localization and Mapping with the Kinect sensor

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. ...


Navigating the Landscape for Real-Time Localization and Mapping for Robotics and Virtual and Augmented Reality

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 ...


A Sparse Separable SLAM Back-End

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 ...


Determining locations of intracardiac basket and probe electrodes from multiplane fluoroscopic images

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.


Vision-based recognition for robot localization

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 ...



Educational Resources on Simultaneous localization and mapping

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IEEE.tv Videos

Emerging Learning and Algorithmic Methods for Data Association in Robotics - ICRA 2020
Localization Services for Online Common Operational Picture and Situation Awareness
Cooperative Localization in Sensor Networks
Robotics History: Narratives and Networks Oral Histories: Jana Kosecka
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
Road-Mapping Session with Deepa Prahalad at Internet Inclusion: Advancing Solutions, Delhi, 2016
Q&A with Jack Gallant: IEEE Brain Podcast, Episode 11
Comparing Modern Multiport VNA vs. Conventional Switch-based VNA: MicroApps 2015 - Keysight Technologies
Clinton Andrews leads the ETAP Forum Vote on the Top Four IoT Issues
Robotics History: Narratives and Networks Oral Histories: Gary Bradsky
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
The Autonomous City Explorer (ACE) Project--Mobile Robot Navigation in Highly Populated Urban Environments
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware - Emre Neftci: 2016 International Conference on Rebooting Computing
NEREID: Systems Design & Heterogeneous Integration: Danilo Demarchi at INC 2019
IEEE Magnetics Distinguished Lecture - Alison B. Flatau

IEEE-USA E-Books

  • Terrain elevation aided visual simultaneous localization and mapping in aerial platforms

    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.

  • An Exploration Strategy for Simultaneous Localization and Mapping in Large Environments

    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.

  • Simultaneous localization and mapping using a robot partner in dynamic environment

    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.

  • Research on feature extraction method based on Simultaneous localization and mapping

    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.

  • Landmark scanning by using infrared sensor for simultaneous localization and mapping application

    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.

  • Online EM for indoor simultaneous localization and mapping

    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.

  • Improvement of the simultaneous localization and map building algorithm applying scaled unscented transformation

    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.

  • Hierarchical Simultaneous Localization and Mapping Based on Discrete Event Systems

    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.

  • A feature matching method for simultaneous localization and mapping

    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.



Standards related to Simultaneous localization and mapping

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No standards are currently tagged "Simultaneous localization and mapping"