Simultaneous localization and mapping

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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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2018 IEEE Statistical Signal Processing Workshop (SSP)

SSP 2018 is the 20th in a series of unique meetings that bring members of the IEEE Signal Processing Society together with researchers from allied fields such as bioinformatics, communications, machine learning, and statistics. The scope of the workshop includes basic theory, methods and algorithms, and applications. SSP is a single-track workshop that includes invited talks, demonstrations and oral and poster presentations of refereed papers.


2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

The aim of this conference is to bring together academics, researchers, engineers and students worldwide to discuss the state-of-the-art technology and to present recent works related to the various aspects of all kinds of robots. Topics include but not limited to: Ubiquitous robots, Ambient intelligence, Network-based robotics, Human-robot interaction, Robot vision and audition, Sensor networks and sensor fusion, Robotic agents, Robot intelligence and learning, Intelligent space/environment technologies, Distributed robotics, Navigation and localization, Robot kinematics and dynamics, Robot control, Robotic mechanism and design, Control architecture and middleware, Haptics and teleoperation, Sensors and actuators, Medical/rehabilitation robotics, Humanoid robots, Field robots, Service robots, Industrial robots, Biorobotics and biomimetics, Robotic media and art, Internet of robotic things, Healthcare & life-care robots, etc.


2017 17th International Conference on Control, Automation and Systems (ICCAS)

Control Theory and Applications,Control Devices and Instruments,Industrial Applications of Control,Sensors and Signal Processing,Artificial Intelligent Systems,Autonomous Vehicle Systems, Navigation, Guidance and Control,Biomedical Instruments and Systems,Information and Networking,Multimedia Systems,Process Control Systems,Civil and Urban Control Systems Human Robot Interaction,Robot Mechanism and Control,Robot Vision,Exoskeletal Robot,Intelligent Robot and Service Robot,Robotic Applications


2017 18th International Conference on Advanced Robotics (ICAR)

Robotics and automation, machine vision and AI


2017 20th International Conference on Information Fusion (Fusion)

The International Conference on Information Fusion is a premier forum for the interchange of the latest research in information fusion and discussion of its impacts on our society. The conference brings together researchers and practitioners from industry and academia to report on the latest scientific and technical advances.


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Periodicals related to Simultaneous localization and mapping

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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 ...


Industrial Electronics, IEEE Transactions on

Theory and applications of industrial electronics and control instrumentation science and engineering, including microprocessor control systems, high-power controls, process control, programmable controllers, numerical and program control systems, flow meters, and identification systems.


Industrial Informatics, IEEE Transactions on

IEEE Transactions on Industrial Informatics focuses on knowledge-based factory automation as a means to enhance industrial fabrication and manufacturing processes. This embraces a collection of techniques that use information analysis, manipulation, and distribution to achieve higher efficiency, effectiveness, reliability, and/or security within the industrial environment. The scope of the Transaction includes reporting, defining, providing a forum for discourse, and informing ...


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Xplore Articles related to Simultaneous localization and mapping

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Integrated machine vision and communication system for blind navigation and guidance

Thomas Gonnot; Jafar Saniie 2016 IEEE International Conference on Electro Information Technology (EIT), 2016

This paper investigates methods and procedures to construct an efficient system to assist blinds in their everyday life. In particular, various technologies that can be utilized to build a wearable system are examined. The machine vision and the communication component of the blind navigation and guidance is designed not only to map the surroundings environment but also to determine a ...


A Curvature based Method to Extract Natural Landmarks for Mobile Robot Navigation

Pedro Nunez; Ricardo Vaizquez; Jose C. del Toro; Antonio Bandera; Francisco Sandoval 2007 IEEE International Symposium on Intelligent Signal Processing, 2007

Landmark extraction is an essential task for robot navigation which not only requires an effective measure, but also the characterisation of landmarks to reduce the subsequent data association ambiguity. This paper describes a new method to detect natural landmarks from the adaptively estimated curvature function associated to 2D laser scans. This set of landmarks is composed of items associated to ...


Long-term place recognition using multi-level words of spatial densities

Renan Maffei; Vitor A. M. Jorge; Vitor F. Rey; Mariana Kolberg; Edson Prestes 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016

Proper place recognition on an environment that can change over time is fundamental for long-term SLAM. In such scenarios the observations obtained in the same region can drastically differ due to changes caused by semi-static objects, such as doors, furniture, etc. In this work, we extend a strategy that represents environment regions using words, based on spatial density information extracted ...


Blind, Adaptive Channel Shortening Equaliser Design with State Information (BACS-SI)

Cenk Toker; Gokhan Altin 2007 IEEE 15th Signal Processing and Communications Applications, 2007

Channel shortening equalisation is a technique which must be used in multicarrier modulation systems in order to maintain the orthogonality of subcarriers. Most of the proposals in the literature require perfect channel state information for this design. In cases where channel estimation is not possible, blind channel shortening equalisation eliminates this requirement. Few proposals which perform blind channel shortening, lack ...


The SLAM algorithm of mobile robot with omnidirectional vision based on EKF

Kaiyu Wang; Guihua Xia; Qidan Zhu; Yongtao Yu; Yebin Wu; Yan Wang 2012 IEEE International Conference on Information and Automation, 2012

An improved simultaneous localization and mapping(SLAM) method based on extended Kalman filter(EKF) is presented to solve the SLAM problem of mobile robot with omnidirectional vision. The environment feature is extracted from the environment information around the mobile robot got by onmidirectional vision, then the landmark is located, finally, the position and attitude of the mobile robot and the map library ...


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Educational Resources on Simultaneous localization and mapping

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eLearning

Integrated machine vision and communication system for blind navigation and guidance

Thomas Gonnot; Jafar Saniie 2016 IEEE International Conference on Electro Information Technology (EIT), 2016

This paper investigates methods and procedures to construct an efficient system to assist blinds in their everyday life. In particular, various technologies that can be utilized to build a wearable system are examined. The machine vision and the communication component of the blind navigation and guidance is designed not only to map the surroundings environment but also to determine a ...


A Curvature based Method to Extract Natural Landmarks for Mobile Robot Navigation

Pedro Nunez; Ricardo Vaizquez; Jose C. del Toro; Antonio Bandera; Francisco Sandoval 2007 IEEE International Symposium on Intelligent Signal Processing, 2007

Landmark extraction is an essential task for robot navigation which not only requires an effective measure, but also the characterisation of landmarks to reduce the subsequent data association ambiguity. This paper describes a new method to detect natural landmarks from the adaptively estimated curvature function associated to 2D laser scans. This set of landmarks is composed of items associated to ...


Long-term place recognition using multi-level words of spatial densities

Renan Maffei; Vitor A. M. Jorge; Vitor F. Rey; Mariana Kolberg; Edson Prestes 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016

Proper place recognition on an environment that can change over time is fundamental for long-term SLAM. In such scenarios the observations obtained in the same region can drastically differ due to changes caused by semi-static objects, such as doors, furniture, etc. In this work, we extend a strategy that represents environment regions using words, based on spatial density information extracted ...


Blind, Adaptive Channel Shortening Equaliser Design with State Information (BACS-SI)

Cenk Toker; Gokhan Altin 2007 IEEE 15th Signal Processing and Communications Applications, 2007

Channel shortening equalisation is a technique which must be used in multicarrier modulation systems in order to maintain the orthogonality of subcarriers. Most of the proposals in the literature require perfect channel state information for this design. In cases where channel estimation is not possible, blind channel shortening equalisation eliminates this requirement. Few proposals which perform blind channel shortening, lack ...


The SLAM algorithm of mobile robot with omnidirectional vision based on EKF

Kaiyu Wang; Guihua Xia; Qidan Zhu; Yongtao Yu; Yebin Wu; Yan Wang 2012 IEEE International Conference on Information and Automation, 2012

An improved simultaneous localization and mapping(SLAM) method based on extended Kalman filter(EKF) is presented to solve the SLAM problem of mobile robot with omnidirectional vision. The environment feature is extracted from the environment information around the mobile robot got by onmidirectional vision, then the landmark is located, finally, the position and attitude of the mobile robot and the map library ...


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IEEE-USA E-Books

  • A Linear Approximation for Graph-Based Simultaneous Localization and Mapping

    This article investigates the problem of Simultaneous Localization and Mapping (SLAM) from the perspective of linear estimation theory. The problem is first formulated in terms of graph embedding: a graph describing robot poses at subsequent instants of time needs be embedded in a three-dimensional space, assuring that the estimated configuration maximizes measurement likelihood. Combining tools belonging to linear estimation and graph theory, a closed-form approximation to the full SLAM problem is proposed, under the assumption that the relative position and the relative orientation measurements are independent. The approach needs no initial guess for optimization and is formally proven to admit solution under the SLAM setup. The resulting estimate can be used as an approximation of the actual nonlinear solution or can be further refined by using it as an initial guess for nonlinear optimization techniques. Finally, the experimental analysis demonstrates that such refinement is often unnecessary, since the linear estimate is already accurate.

  • A Tree Parameterization for Efficiently Computing Maximum Likelihood Maps using Gradient Descent

    In 2006, Olson et al. presented a novel approach to address the graph-based simultaneous localization and mapping problem by applying stochastic gradient descent to minimize the error introduced by constraints. Together with multi- level relaxation, this is one of the most robust and efficient maximum likelihood techniques published so far. In this paper, we present an extension of Olson's algorithm. It applies a novel parameterization of the nodes in the graph that significantly improves the performance and enables us to cope with arbitrary network topologies. The latter allows us to bound the complexity of the algorithm to the size of the mapped area and not to the length of the trajectory as it is the case with both previous approaches. We implemented our technique and compared it to multi-level relaxation and Olson's algorithm. As we demonstrate in simulated and in real world experiments, our approach converges faster than the other approaches and yields accurate maps of the environment.

  • Mapping Large Loops with a Single Hand-Held Camera

    This paper presents a method for Simultaneous Localization and Mapping (SLAM), relying on a monocular camera as the only sensor, which is able to build outdoor, closed-loop maps much larger than previously achieved with such input. Our system, based on the Hierarchical Map approach [1], builds independent local maps in real-time using the EKF-SLAM technique and the inverse depth representation proposed in [2]. The main novelty in the local mapping process is the use of a data association technique that greatly improves its robustness in dynamic and complex environments. A new visual map matching algorithm stitches these maps together and is able to detect large loops automatically, taking into account the unobservability of scale intrinsic to pure monocular SLAM. The loop closing constraint is applied at the upper level of the Hierarchical Map in near real-time. We present experimental results demonstrating monocular SLAM as a human carries a camera over long walked trajectories in outdoor areas with people and other clutter, even in the more difficult case of forward-looking camera, and show the closing of loops of several hundred meters.

  • Improving Localization Robustness in Monocular SLAM Using a High-Speed Camera

    In the robotics community localization and mapping of an unknown environment is a well-studied problem. To solve this problem in real-time using visual input, a standard monocular Simultaneous Localization and Mapping (SLAM) algorithm can be used. This algorithm is very stable when smooth motion is expected, but in case of erratic or sudden movements, the camera pose typically gets lost. To improve robustness in Monocular SLAM (MonoSLAM) we propose to use a camera with faster readout speed to obtain a frame rate of 200Hz. We further present an extended MonoSLAM motion model, which can handle movements with significant jitter. In this work the improved localization and mapping have been evaluated against ground truth, which is reconstructed from off-line vision. To explain the benefits of using a high frame rate vision input in MonoSLAM framework, we performed repeatable experiments with a high- speed camera mounted onto a robotic arm. Due to the dense visual information MonoSLAM can faster shrink localization and mapping uncertainties and can operate under fast, erratic, or sudden movements. The extended motion model can provide additional robustness against significant handheld jitter when throwing or shaking the camera.

  • BS-SLAM: Shaping the World

    This paper presents BS-SLAM, a simultaneous localization and mapping algorithm for use in unstructured environments that is effective regardless of whether features correspond to simple geometric primitives such as lines or not. The coordinates of the control points defining a set of B-splines are used to form a complete and compact description of the environment, thus making it feasible to use an extended Kalman filter based SLAM algorithm. The proposed method is the first known EKF-SLAM implementation capable of describing both straight and curve features in a parametric way. Appropriate observation equation that allows the exploitation of virtually all observations from a range sensor such as the ubiquitous laser range finder is developed. Efficient strategies for computing the relevant Jacobians, perform data association, initialization and expanding the map are presented. The effectiveness of the algorithms is demonstrated using experimental data.

  • On the Structure of Nonlinearities in Pose Graph SLAM

    Pose graphs have become an attractive representation for solving Simultaneous Localization and Mapping (SLAM) problems. In this paper, we analyze the structure of the nonlinearities in the 2D SLAM problem formulated as the optimizing of a pose graph. First, we prove that finding the optimal configuration of a very basic pose graph with 3 nodes (poses) and 3 edges (relative pose constraints) with spherical covariance matrices, which can be formulated as a six dimensional least squares optimization problem, is equivalent to solving a one dimensional optimization problem. Then we show that the same result can be extended to the optimizing of a pose graph with "two anchor nodes" where every edge is connecting to one of the two anchor nodes. Furthermore, we prove that the global minimum of the resulting one dimensional optimization problem must belong to a certain interval and there are at most 3 minima in that interval. Thus the globally optimal pose configuration of the pose graph can be obtained very easily through the bisection method and closed-form formulas.



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