Relevant bibliographies by topics / Simultaneuos localization and mapping (SLAM)

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Academic literature on the topic 'Simultaneuos localization and mapping (SLAM)'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Simultaneuos localization and mapping (SLAM)"

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Saat, Shahrizal, AN MF Airini, Muhammad Salihin Saealal, A. R. Wan Norhisyam, and M. S. Farees Ezwan. "Hector SLAM 2D Mapping for Simultaneous Localization and Mapping (SLAM)." Journal of Engineering and Applied Sciences 14, no. 16 (November 10, 2019): 5610–15. http://dx.doi.org/10.36478/jeasci.2019.5610.5615.

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Tsubouchi, Takashi. "Introduction to Simultaneous Localization and Mapping." Journal of Robotics and Mechatronics 31, no. 3 (June 20, 2019): 367–74. http://dx.doi.org/10.20965/jrm.2019.p0367.

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Simultaneous localization and mapping (SLAM) forms the core of the technology that supports mobile robots. With SLAM, when a robot is moving in an actual environment, real world information is imported to a computer on the robot via a sensor, and robot’s physical location and a map of its surrounding environment of the robot are created. SLAM is a major topic in mobile robot research. Although the information, supported by a mathematical description, is derived from a space in reality, it is formulated based on a probability theory when being handled. Therefore, this concept contributes not only to the research and development concerning mobile robots, but also to the training of mathematics and computer implementation, aimed mainly at position estimation and map creation for the mobile robots. This article focuses on the SLAM technology, including a brief overview of its history, insights from the author, and, finally, introduction of a specific example that the author was involved.
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Boal, Jaime, Álvaro Sánchez-Miralles, and Álvaro Arranz. "Topological simultaneous localization and mapping: a survey." Robotica 32, no. 5 (December 3, 2013): 803–21. http://dx.doi.org/10.1017/s0263574713001070.

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SUMMARYOne of the main challenges in robotics is navigating autonomously through large, unknown, and unstructured environments. Simultaneous localization and mapping (SLAM) is currently regarded as a viable solution for this problem. As the traditional metric approach to SLAM is experiencing computational difficulties when exploring large areas, increasing attention is being paid to topological SLAM, which is bound to provide sufficiently accurate location estimates, while being significantly less computationally demanding. This paper intends to provide an introductory overview of the most prominent techniques that have been applied to topological SLAM in terms of feature detection, map matching, and map fusion.
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Skrzypczyński, Piotr. "Simultaneous localization and mapping: A feature-based probabilistic approach." International Journal of Applied Mathematics and Computer Science 19, no. 4 (December 1, 2009): 575–88. http://dx.doi.org/10.2478/v10006-009-0045-z.

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Simultaneous localization and mapping: A feature-based probabilistic approachThis article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.
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Alsadik, Bashar, and Samer Karam. "The Simultaneous Localization and Mapping (SLAM)-An Overview." Surveying and Geospatial Engineering Journal 2, no. 01 (May 18, 2021): 01–12. http://dx.doi.org/10.38094/sgej1027.

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Positioning is a need for many applications related to mapping and navigation either in civilian or military domains. The significant developments in satellite-based techniques, sensors, telecommunications, computer hardware and software, image processing, etc. positively influenced to solve the positioning problem efficiently and instantaneously. Accordingly, the mentioned development empowered the applications and advancement of autonomous navigation. One of the most interesting developed positioning techniques is what is called in robotics as the Simultaneous Localization and Mapping SLAM. The SLAM problem solution has witnessed a quick improvement in the last decades either using active sensors like the RAdio Detection And Ranging (Radar) and Light Detection and Ranging (LiDAR) or passive sensors like cameras. Definitely, positioning and mapping is one of the main tasks for Geomatics engineers, and therefore it's of high importance for them to understand the SLAM topic which is not easy because of the huge documentation and algorithms available and the various SLAM solutions in terms of the mathematical models, complexity, the sensors used, and the type of applications. In this paper, a clear and simplified explanation is introduced about SLAM from a Geomatical viewpoint avoiding going into the complicated algorithmic details behind the presented techniques. In this way, a general overview of SLAM is presented showing the relationship between its different components and stages like the core part of the front-end and back-end and their relation to the SLAM paradigm. Furthermore, we explain the major mathematical techniques of filtering and pose graph optimization either using visual or LiDAR SLAM and introduce a summary of the deep learning efficient contribution to the SLAM problem. Finally, we address examples of some existing practical applications of SLAM in our reality.
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NGUYEN, DuyHinh, Xiqian WU, Daisuke IWAKURA, and Kenzo NONAMI. "2C11 Autonomous control and Simultaneous Localization and Mapping (SLAM) of Unmanned Ground Vehicle." Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _2C11–1_—_2C11–9_. http://dx.doi.org/10.1299/jsmemovic.2010._2c11-1_.

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Xu, S., Z. Ji, D. T. Pham, and F. Yu. "Simultaneous localization and mapping: swarm robot mutual localization and sonar arc bidirectional carving mapping." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 225, no. 3 (September 10, 2010): 733–44. http://dx.doi.org/10.1243/09544062jmes2239.

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This work primarily aims to study robot swarm global mapping in a static indoor environment. Due to the prerequisite estimation of the robots' own poses, it is upgraded to a simultaneous localization and mapping (SLAM) problem. Five techniques are proposed to solve the SLAM problem, including the extended Kalman filter (EKF)-based mutual localization, sonar arc bidirectional carving mapping, grid-oriented correlation, working robot group substitution, and termination rule. The EKF mutual localization algorithm updates the pose estimates of not only the current robot, but also the landmark-functioned robots. The arc-carving mapping algorithm is to increase the azimuth resolution of sonar readings by using their freespace regions to shrink the possible regions. It is further improved in both accuracy and efficiency by the creative ideas of bidirectional carving, grid-orientedly correlated-arc carving, working robot group substitution, and termination rule. Software simulation and hardware experiment have verified the feasibility of the proposed SLAM philosophy when implemented in a typical medium-cluttered office by a team of three robots. Besides the combined effect, individual algorithm components have also been investigated.
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Bailey, T., and H. Durrant-Whyte. "Simultaneous localization and mapping (SLAM): part II." IEEE Robotics & Automation Magazine 13, no. 3 (September 2006): 108–17. http://dx.doi.org/10.1109/mra.2006.1678144.

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Saat, Shahrizal, WN Abd Rashid, MZM Tumari, and MS Saealal. "HECTORSLAM 2D MAPPING FOR SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM)." Journal of Physics: Conference Series 1529 (April 2020): 042032. http://dx.doi.org/10.1088/1742-6596/1529/4/042032.

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Debeunne, César, and Damien Vivet. "A Review of Visual-LiDAR Fusion based Simultaneous Localization and Mapping." Sensors 20, no. 7 (April 7, 2020): 2068. http://dx.doi.org/10.3390/s20072068.

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Autonomous navigation requires both a precise and robust mapping and localization solution. In this context, Simultaneous Localization and Mapping (SLAM) is a very well-suited solution. SLAM is used for many applications including mobile robotics, self-driving cars, unmanned aerial vehicles, or autonomous underwater vehicles. In these domains, both visual and visual-IMU SLAM are well studied, and improvements are regularly proposed in the literature. However, LiDAR-SLAM techniques seem to be relatively the same as ten or twenty years ago. Moreover, few research works focus on vision-LiDAR approaches, whereas such a fusion would have many advantages. Indeed, hybridized solutions offer improvements in the performance of SLAM, especially with respect to aggressive motion, lack of light, or lack of visual features. This study provides a comprehensive survey on visual-LiDAR SLAM. After a summary of the basic idea of SLAM and its implementation, we give a complete review of the state-of-the-art of SLAM research, focusing on solutions using vision, LiDAR, and a sensor fusion of both modalities.
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Dissertations / Theses on the topic "Simultaneuos localization and mapping (SLAM)"

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Naghi, Nour. "Simultaneous Localization and Mapping Technologies." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17852/.

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Il problema dello SLAM (Simultaneous Localization And Mapping) consiste nel mappare un ambiente sconosciuto per mezzo di un dispositivo che si muove al suo interno, mentre si effettua la localizzazione di quest'ultimo. All'interno di questa tesi viene analizzato il problema dello SLAM e le differenze che lo contraddistinguono dai problemi di mapping e di localizzazione trattati separatamente. In seguito, si effettua una analisi dei principali algoritmi impiegati al giorno d'oggi per la sua risoluzione, ovvero i filtri estesi di Kalman e i particle filter. Si analizzano poi le diverse tecnologie implementative esistenti, tra le quali figurano sistemi SONAR, sistemi LASER, sistemi di visione e sistemi RADAR; questi ultimi, allo stato dell'arte, impiegano onde millimetriche (mmW) e a banda larga (UWB), ma anche tecnologie radio già affermate, fra le quali il Wi-Fi. Infine, vengono effettuate delle simulazioni di tecnologie basate su sistema di visione e su sistema LASER, con l'ausilio di due pacchetti open source di MATLAB. Successivamente, il pacchetto progettato per sistemi LASER è stato modificato al fine di simulare una tecnologia SLAM basata su segnali Wi-Fi. L'utilizzo di tecnologie a basso costo e ampiamente diffuse come il Wi-Fi apre alla possibilità, in un prossimo futuro, di effettuare localizzazione indoor a basso costo, sfruttando l'infrastruttura esistente, mediante un semplice smartphone. Più in prospettiva, l'avvento della tecnologia ad onde millimetriche (5G) consentirà di raggiungere prestazioni maggiori.
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Sünderhauf, Niko. "Robust Optimization for Simultaneous Localization and Mapping." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-86443.

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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far. The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets. This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.
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Pomerleau, François. "Registration algorithm optimized for simultaneous localization and mapping." Mémoire, Université de Sherbrooke, 2008. http://savoirs.usherbrooke.ca/handle/11143/1465.

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Building maps within an unknown environment while keeping track of the current position is a major step to accomplish safe and autonomous robot navigation. Within the last 20 years, Simultaneous Localization And Mapping (SLAM) became a topic of great interest in robotics. The basic idea of this technique is to combine proprioceptive robot motion information with external environmental information to minimize global positioning errors. Because the robot is moving in its environment, exteroceptive data comes from different points of view and must be expressed in the same coordinate system to be combined. The latter process is called registration. Iterative Closest Point (ICP) is a registration algorithm with very good performances in several 3D model reconstruction applications, and was recently applied to SLAM. However, SLAM has specific needs in terms of real-time and robustness comparatively to 3D model reconstructions, leaving room for specialized robotic mapping optimizations in relation to robot mapping. After reviewing existing SLAM approaches, this thesis introduces a new registration variant called Kd-ICP. This referencing technique iteratively decreases the error between misaligned point clouds without extracting specific environmental features. Results demonstrate that the new rejection technique used to achieve mapping registration is more robust to large initial positioning errors. Experiments with simulated and real environments suggest that Kd-ICP is more robust compared to other ICP variants. Moreover, the Kd-ICP is fast enough for real-time applications and is able to deal with sensor occlusions and partially overlapping maps. Realizing fast and robust local map registrations opens the door to new opportunities in SLAM. It becomes feasible to minimize the cumulation of robot positioning errors, to fuse local environmental information, to reduce memory usage when the robot is revisiting the same location. It is also possible to evaluate network constrains needed to minimize global mapping errors.
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Inostroza, Ferrari Felipe Ignacio. "The estimation of detection statistics in simultaneus localization and mapping." Tesis, Universidad de Chile, 2015. http://repositorio.uchile.cl/handle/2250/134725.

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Magíster en Ciencias de la Ingeniería, Mención Ingeniería Eléctrica
Ingeniero Civil Eléctrico
El uso de Conjuntos Aleatorios Finitos (RFS por su sigla en inglés) tiene varias ventajas respecto de los métodos tradicionales basados en vectores. Entre ellas están el incluir las estadísticas de detección del sensor y la eliminación de las heurísticas tanto para la asociación de datos como para la inicialización y eliminación de objetos en mapa. Para obtener los beneficios de los estimadores basados en RFS en el problema de Construcción de Mapas y Localización Simultanea (SLAM por su acrónimo en inglés), las estadísticas de detección y falsa alarma del extractor de características deben ser modeladas y utilizadas en cada actualización del mapa. Esta Tesis presenta técnicas para obtener estas estadísticas en el caso de características semánticas extraídas de mediciones láser. Además se concentra en la extracción de objetos cilíndricos, como pilares, árboles y postes de luz, en ambientes exteriores. Las estadísticas de detección obtenidas son utilizadas dentro de una solución a SLAM basada en RFS, conocida como Rao-Blackwellized (RB)-probability hypothesis density (PHD)-SLAM, y el algoritmo multiple hypothesis (MH)-factored solution to SLAM (FastSLAM), solución a SLAM basada en vectores. El desempeño de cada algoritmo al usar estas estadísticas es comparado con el de utilizar estadísticas constantes. Los resultados muestran las ventajas de modelar las estadísticas de detección, particularmente en el caso del paradigma RFS. En particular, el error en las estimaciones del mapa, medido utilizando la distancia optimal sub- pattern assignment (OSPA) a un mapa ground truth generado de forma independiente, disminuye en un 13% en el caso de MH-FastSLAM y en un 13% para RB-PHD-SLAM al modelar las estadísticas de detección. A pesar de que no se tiene un ground truth para la trayectoria del robot, se evalúan las trayectorias visualmente, encontradose estimaciones superiores para el método propuesto. Por lo tanto, se concluye que el modelamiento de las estadísticas de detección es de gran importancia al implementar una aplicación de SLAM.
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Bao, Guanqun. "On Simultaneous Localization and Mapping inside the Human Body (Body-SLAM)." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-dissertations/206.

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Wireless capsule endoscopy (WCE) offers a patient-friendly, non-invasive and painless investigation of the entire small intestine, where other conventional wired endoscopic instruments can barely reach. As a critical component of the capsule endoscopic examination, physicians need to know the precise position of the endoscopic capsule in order to identify the position of intestinal disease after it is detected by the video source. To define the position of the endoscopic capsule, we need to have a map of inside the human body. However, since the shape of the small intestine is extremely complex and the RF signal propagates differently in the non-homogeneous body tissues, accurate mapping and localization inside small intestine is very challenging. In this dissertation, we present an in-body simultaneous localization and mapping technique (Body-SLAM) to enhance the positioning accuracy of the WCE inside the small intestine and reconstruct the trajectory the capsule has traveled. In this way, the positions of the intestinal diseases can be accurately located on the map of inside human body, therefore, facilitates the following up therapeutic operations. The proposed approach takes advantage of data fusion from two sources that come with the WCE: image sequences captured by the WCE's embedded camera and the RF signal emitted by the capsule. This approach estimates the speed and orientation of the endoscopic capsule by analyzing displacements of feature points between consecutive images. Then, it integrates this motion information with the RF measurements by employing a Kalman filter to smooth the localization results and generate the route that the WCE has traveled. The performance of the proposed motion tracking algorithm is validated using empirical data from the patients and this motion model is later imported into a virtual testbed to test the performance of the alternative Body-SLAM algorithms. Experimental results show that the proposed Body-SLAM technique is able to provide accurate tracking of the WCE with average error of less than 2.3cm.
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Tiranti, Luca. "Simultaneous localization and mapping using radar images." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22893/.

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Questa tesi affronta il problema di localizzazione e mappatura simultanea in locali indoor utilizzando la tecnologia radar a onde millimetriche. Gli scenari considerati e le tecnologie impiegate sono in linea con il concetto di “personal mobile radar” rendendo questo lavoro un proof-of-concept di tale idea, testandone le performance in ambienti reali attraverso differenti campagne di misura. In accordo con tale concetto, sarà possibile integrare in dispositivi mobili, quali smartphone e tablet, array di antenne che scansioneranno autonomamente l’ambiente circostante e permetteranno di raggiungere una soluzione con specifiche simili alle più performanti soluzioni di SLAM come la tecnologia laser o lidar. Al contempo, l’utilizzo di tecnologia a onde millimetriche permette un possibile impiego del radar personale anche in ambienti con scarsa visibilità trovando applicazione, ad esempio, in contesti industriali ma anche per la sicurezza delle persone mantenendo i costi contenuti ed evitando l'installazione di infrastrutture ad-hoc. Lo campagne di misura in locali indoor svolte per questa tesi hanno reso possibile la raccolta di dati, i quali successivamente sono stati processati e forniti in input all'algoritmo di SLAM proposto sotto forma di “immagini radar”. A partire da queste, verranno presentate diverse strategie sviluppate per la stima della traiettoria del radar e mapping dell’ambiente e, infine, per ognuna di queste, verranno mostrati i relativi risultati ottenuti. Verranno mostrati i risultati ottenuti da campagne di misure sia a 77 GHz che a 300 GHz. Queste ultime sono state condotte presso il centro di ricerca CEA-Leti (Grenoble, France) nel contesto del progetto europeo PRIMELOC (Personal Radars for Radio Imaging and Infrastructure-less Localization) del quale l’Università di Bologna è coordinatrice. I risultati mostreranno come l’aumento delle frequenze in gioco possa portare benefici in termini di accuratezza dei risultati.
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Pereira, Savio Joseph. "On the utilization of Simultaneous Localization and Mapping(SLAM) along with vehicle dynamics in Mobile Road Mapping Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/94425.

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Mobile Road Mapping Systems (MRMS) are the current solution to the growing demand for high definition road surface maps in wide ranging applications from pavement management to autonomous vehicle testing. The focus of this research work is to improve the accuracy of MRMS by using the principles of Simultaneous Localization and Mapping (SLAM). First a framework for describing the sensor measurement models in MRMS is developed. Next the problem of estimating the road surface from the set of sensor measurements is formulated as a SLAM problem and two approaches are proposed to solve the formulated problem. The first is an incremental solution wherein sensor measurements are processed in sequence using an Extended Kalman Filter (EKF). The second is a post-processing solution wherein the SLAM problem is formulated as an inference problem over a factor graph and existing factor graph SLAM techniques are used to solve the problem. For the mobile road mapping problem, the road surface being measured is one the primary inputs to the dynamics of the MRMS. Hence, concurrent to the main objective this work also investigates the use of the dynamics of the host vehicle of the system to improve the accuracy of the MRMS. Finally a novel method that builds off the concepts of the popular model fitting algorithm, Random Sampling and Consensus (RANSAC), is developed in order to identify outliers in road surface measurements and estimate the road elevations at grid nodes using these measurements. The developed methods are validated in a simulated environment and the results demonstrate a significant improvement in the accuracy of MRMS over current state-of-the art methods.
Doctor of Philosophy
Mobile Road Mapping Systems (MRMS) are the current solution to the growing demand for high definition road surface maps in wide ranging applications from pavement management to autonomous vehicle testing. The objective of this research work is to improve the accuracy of MRMS by investigating methods to improve the sensor data fusion process. The main focus of this work is to apply the principles from the field of Simultaneous Localization and Mapping (SLAM) in order to improve the accuracy of MRMS. The concept of SLAM has been successfully applied to the field of mobile robot navigation and thus the motivation of this work is to investigate its application to the problem of mobile road mapping. For the mobile road mapping problem, the road surface being measured is one the primary inputs to the dynamics of the MRMS. Hence this work also investigates whether knowledge regarding the dynamics of the system can be used to improve the accuracy. Also developed as part of this work is a novel method for identifying outliers in road surface datasets and estimating elevations at road surface grid nodes. The developed methods are validated in a simulated environment and the results demonstrate a significant improvement in the accuracy of MRMS over current state-of-the-art methods.
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Desrochers, Benoît. "Simultaneous localization and mapping in unstructured environments : a set-membership approach." Thesis, Brest, École nationale supérieure de techniques avancées Bretagne, 2018. http://www.theses.fr/2018ENTA0006/document.

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Cette thèse étudie le problème de la localisation et de la cartographie simultanée (SLAM), dans des environnements non structurés, c'est-à-dire, qui ne peuvent pas être décrits par des équations ou des formes géométriques. Ces types d'environnements sont souvent rencontrés dans le domaine sous-marin. Contrairement aux approches classiques, l'environnement n'est pas modélisé par une collection de descripteurs ou d'amers ponctuels, mais directement par des ensembles. Ces ensembles, appelés forme ou shape, sont associés à des caractéristiques physiques de l'environnement, comme par exemple, des textures, du relief ou, de manière plus symbolique, à l'espace libre autour du véhicule. D'un point de vue théorique, le problème du SLAM, basé sur des formes, est formalisé par un réseau de contraintes hybrides dont les variables sont des vecteurs de Rn et des sous-ensembles de Rn. De la même façon que l'incertitude sur une variable réelle est représentée par un intervalle de réels, l'incertitude sur les formes sera représentée par un intervalle de forme. La principale contribution de cette thèse est de proposer un formalisme, basé sur le calcul par intervalle, capable de calculer ces domaines. En application, les algorithmes développés ont été appliqués au problème du SLAM à partir de données bathymétriques recueillies par un véhicule sous-marin autonome (AUV)
This thesis deals with the simultaneous localization and mapping (SLAM) problem in unstructured environments, i.e. which cannot be described by geometrical features. This type of environment frequently occurs in an underwater context.Unlike classical approaches, the environment is not described by a collection of punctual features or landmarks, but directly by sets. These sets, called shapes, are associated with physical features such as the relief, some textures or, in a more symbolic way, the space free of obstacles that can be sensed around a robot. In a theoretical point of view, the SLAM problem is formalized as an hybrid constraint network where the variables are vectors and subsets of Rn. Whereas an uncertain real number is enclosed in an interval, an uncertain shape is enclosed in an interval of sets. The main contribution of this thesis is the introduction of a new formalism, based on interval analysis, able to deal with these domains. As an application, we illustrate our method on a SLAM problem based on bathymetric data acquired by an autonomous underwater vehicle (AUV)
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Lee, Chun-Fan Computer Science &amp Engineering Faculty of Engineering UNSW. "Towards topological mapping with vision-based simultaneous localization and map building." Awarded by:University of New South Wales. Computer Science & Engineering, 2008. http://handle.unsw.edu.au/1959.4/41551.

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Although the theory of Simultaneous Localization and Map Building (SLAM) is well developed, there are many challenges to overcome when incorporating vision sensors into SLAM systems. Visual sensors have different properties when compared to range finding sensors and therefore require different considerations. Existing vision-based SLAM algorithms extract point landmarks, which are required for SLAM algorithms such as the Kalman filter. Under this restriction, the types of image features that can be used are limited and the full advantages of vision not realized. This thesis examines the theoretical formulation of the SLAM problem and the characteristics of visual information in the SLAM domain. It also examines different representations of uncertainty, features and environments. It identifies the necessity to develop a suitable framework for vision-based SLAM systems and proposes a framework called VisionSLAM, which utilizes an appearance-based landmark representation and topological map structure to model metric relations between landmarks. A set of Haar feature filters are used to extract image structure statistics, which are robust against illumination changes, have good uniqueness property and can be computed in real time. The algorithm is able to resolve and correct false data associations and is robust against random correlation resulting from perceptual aliasing. The algorithm has been tested extensively in a natural outdoor environment.
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Vallivaara, I. (Ilari). "Simultaneous localization and mapping using the indoor magnetic field." Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526217741.

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Abstract The Earth’s magnetic field (MF) has been used for navigation for centuries. Man-made metallic structures, such as steel reinforcements in buildings, cause local distortions to the Earth’s magnetic field. Up until the recent decade, these distortions have been mostly considered as a source of error in indoor localization, as they interfere with the compass direction. However, as the distortions are temporally stable and spatially distinctive, they provide a unique magnetic landscape that can be used for constructing a map for indoor localization purposes, as noted by recent research in the field. Most approaches rely on manually collecting the magnetic field map, a process that can be both tedious and error-prone. In this thesis, the map is collected by a robotic platform with minimal sensor equipment. It is shown that a mere magnetometer along with odometric information suffices to construct the map via a simultaneous localization and mapping (SLAM) procedure that builds on the Rao-Blackwellized particle filter as means for recursive Bayesian estimation. Furthermore, the maps are shown to achieve decimeter level localization accuracy that combined with the extremely low-cost hardware requirements makes the presented methods very lucrative for domestic robots. In addition, general auxiliary methods for effective sampling and dealing with uncertainties are presented. Although the methods presented here are devised in mobile robotics context, most of them are also applicable to mobile device-based localization, for example, with little modifications. Magnetic field localization offers a promising alternative to WiFi-based methods for achieving GPS-level localization indoors. This is motivated by the rapidly growing indoor location market
Tiivistelmä Maan magneettikenttään perustuvat kompassit ovat ohjanneet merenkäyntiä vuosisatojen ajan. Rakennusten metallirakenteet aiheuttavat paikallisia häiriöitä tähän magneettikenttään, minkä vuoksi kompasseja on pidetty epäluotettavina sisätiloissa. Vasta viimeisen vuosikymmenen aikana on huomattu, että koska nämä häiriöt ovat ajallisesti pysyviä ja paikallisesti hyvin erottelevia, niistä voidaan muodostaa jokaiselle rakennukselle yksilöllinen häiriöihin perustuva magneettinen kartta, jota voidaan käyttää sisätiloissa paikantamiseen. Suurin osa tämänhetkisistä magneettikarttojen sovelluksista perustuu kartan käsin keräämiseen, mikä on sekä työlästä että tarjoaa mahdollisuuden inhimillisiin virheisiin. Tämä väitöstutkimus tarttuu ongelmaan laittamalla robotin hoitamaan kartoitustyön ja näyttää, että robotti pystyy itsenäisesti keräämään magneettisen kartan hyödyntäen pelkästään magnetometriä ja renkaiden antamia matkalukemia. Ratkaisu perustuu faktoroituun partikkelisuodattimeen (RBPF), joka approksimoi täsmällistä rekursiivista bayesilaista ratkaisua. Robotin keräämien karttojen tarkkuus mahdollistaa paikannuksen n. 10 senttimetrin tarkkuudella. Vähäisten sensori- ja muiden vaatimusten takia menetelmä soveltuu erityisen hyvin koti- ja parvirobotiikkaan, joissa hinta on usein ratkaiseva tekijä. Tutkimuksessa esitellään lisäksi uusia apumenetelmiä tehokkaaseen näytteistykseen ja epävarmuuden hallintaan. Näiden käyttöala ei rajoitu pelkästään magneettipaikannukseen- ja kartoitukseen. Robotiikan sovellusten lisäksi tutkimusta motivoi voimakkaasti kasvava tarve älylaitteissa toimivalle sisätilapaikannukselle. Tämä avaa uusia mahdollisuuksia paikannukselle ympäristöissä, joissa GPS ei perinteisesti toimi
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Books on the topic "Simultaneuos localization and mapping (SLAM)"

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Sanfeliu, Alberto, and Juan Andrade Cetto. Environment Learning for Indoor Mobile Robots: A Stochastic State Estimation Approach to Simultaneous Localization and Map Building. Springer, 2010.

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Erdem, Uğur Murat, Nicholas Roy, John J. Leonard, and Michael E. Hasselmo. Spatial and episodic memory. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0029.

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The neuroscience of spatial memory is one of the most promising areas for developing biomimetic solutions to complex engineering challenges. Grid cells are neurons recorded in the medial entorhinal cortex that fire when rats are in an array of locations in the environment falling on the vertices of tightly packed equilateral triangles. Grid cells suggest an exciting new approach for enhancing robot simultaneous localization and mapping (SLAM) in changing environments and could provide a common map for situational awareness between human and robotic teammates. Current models of grid cells are well suited to robotics, as they utilize input from self-motion and sensory flow similar to inertial sensors and visual odometry in robots. Computational models, supported by in vivo neural activity data, demonstrate how grid cell representations could provide a substrate for goal-directed behavior using hierarchical forward planning that finds novel shortcut trajectories in changing environments.
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Andrade-Cetto, Juan, and Alberto Sanfeliu. Environment Learning for Indoor Mobile Robots: A Stochastic State Estimation Approach to Simultaneous Localization and Map Building (Springer Tracts in Advanced Robotics). Springer, 2006.

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Book chapters on the topic "Simultaneuos localization and mapping (SLAM)"

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Berns, Karsten, and Ewald von Puttkamer. "Simultaneous localization and mapping (SLAM)." In Autonomous Land Vehicles, 146–72. Wiesbaden: Vieweg+Teubner, 2009. http://dx.doi.org/10.1007/978-3-8348-9334-5_6.

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Perera, Samunda, Dr Nick Barnes, and Dr Alexander Zelinsky. "Exploration: Simultaneous Localization and Mapping (SLAM)." In Computer Vision, 268–75. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_280.

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Chatterjee, Amitava, Anjan Rakshit, and N. Nirmal Singh. "Simultaneous Localization and Mapping (SLAM) in Mobile Robots." In Vision Based Autonomous Robot Navigation, 167–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33965-3_7.

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Kung, Da-Wei, Chen-Chien Hsu, Wei-Yen Wang, and Jacky Baltes. "Adaptive Computation Algorithm for Simultaneous Localization and Mapping (SLAM)." In Advances in Intelligent Systems and Computing, 75–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31293-4_7.

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Huang, Teng-Wei, Chen-Chien Hsu, Wei-Yen Wang, and Jacky Baltes. "ROSLAM—A Faster Algorithm for Simultaneous Localization and Mapping (SLAM)." In Advances in Intelligent Systems and Computing, 65–74. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31293-4_6.

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Yeh, Chun-Hsiao, Herng-Hua Chang, Chen-Chien Hsu, and Wei-Yen Wang. "Simultaneous Localization and Mapping with a Dynamic Switching Mechanism (SLAM-DSM)." In Advances in Intelligent Systems and Computing, 55–64. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31293-4_5.

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Thusabantu, Nyasha Fadzai, and G. Vadivu. "Adoption of Big Data Streaming Techniques for Simultaneous Localization and Mapping (SLAM) in IoT-Aided Robotics Devices." In Cognitive Informatics and Soft Computing, 315–20. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0617-4_31.

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Ellery, Alex. "Autonomous Navigation—Self-localization and Mapping (SLAM)." In Planetary Rovers, 331–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-642-03259-2_9.

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Joukhadar, Abdulkader, Dalia Kass Hanna, Andreas Müller, and Christoph Stöger. "UKF-Assisted SLAM for 4WDDMR Localization and Mapping." In Mechanism, Machine, Robotics and Mechatronics Sciences, 259–70. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89911-4_19.

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Mo, Hongwei, Xiaosen Chen, Kai Wang, and Haoran Wang. "Autonomous Localization and Mapping for Mobile Robot Based on ORB-SLAM." In Proceedings of 2018 Chinese Intelligent Systems Conference, 749–60. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2288-4_71.

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Conference papers on the topic "Simultaneuos localization and mapping (SLAM)"

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Khairuddin, Alif Ridzuan, Mohamad Shukor Talib, and Habibollah Haron. "Review on simultaneous localization and mapping (SLAM)." In 2015 IEEE International Conference on Control System, Computing and Engineering (ICCSCE). IEEE, 2015. http://dx.doi.org/10.1109/iccsce.2015.7482163.

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Shih, Yan-Jhang, Chen-Chien Hsu, Wei-Yen Wang, and Yin-Tien Wang. "Feature extracted algorithm for simultaneous localization and mapping (SLAM)." In 2015 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2015. http://dx.doi.org/10.1109/icce.2015.7066497.

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Ortiz, Salvador, Wen Yu, and Erik Zamora. "Sliding Mode SLAM for Robust Simultaneous Localization and Mapping." In IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2018. http://dx.doi.org/10.1109/iecon.2018.8591121.

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Tuna, Gurkan, Kayhan Gulez, V. Cagri Gungor, and T. Veli Mumcu. "Evaluations of different Simultaneous Localization and Mapping (SLAM) algorithms." In IECON 2012 - 38th Annual Conference of IEEE Industrial Electronics. IEEE, 2012. http://dx.doi.org/10.1109/iecon.2012.6389151.

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Chin, Wei Hong, and Chu Kiong Loo. "Topological Gaussian ARAM for Simultaneous Localization and Mapping (SLAM)." In 2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS). IEEE, 2012. http://dx.doi.org/10.1109/mhs.2012.6492468.

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Cheng-Kai Yang, Chen-Chien Hsu, and Yin-Tien Wang. "Computationally efficient algorithm for simultaneous localization and mapping (SLAM)." In 2013 IEEE 10th International Conference on Networking, Sensing and Control (ICNSC 2013). IEEE, 2013. http://dx.doi.org/10.1109/icnsc.2013.6548759.

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Rosa, Paulo, Onias Silveira, João De Melo, Leandro Moreira, and Luiz Rodrigues. "Development of Embedded Algorithm for Visual Simultaneous Localization and Mapping." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8319.

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The Simultaneous Localization and Mapping (SLAM) problem is recurrent in today's robotics. One challenge of it is the extensive computational cost to create complex maps in real-time. Various applications, mainly search and rescue operate in GPS denied scenarios, with possible difficulty communicating with an external base. A portable SLAM system capable of being run in a microcomputer would greatly help such operations. This paper mentions the unfinished into this topic and discusses further steps that shall be taken in the upcoming months.
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Herath, Damith C., S. Kodagoda, and Gamini Dissanayake. "New framework for Simultaneous Localization and Mapping: Multi map SLAM." In 2008 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2008. http://dx.doi.org/10.1109/robot.2008.4543483.

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Toivanen, Pekka, Vandad Imani, and Keijo Haataja. "Three main paradigms of simultaneous localization and mapping (SLAM) problem." In Tenth International Conference on Machine Vision (ICMV 2017), edited by Jianhong Zhou, Petia Radeva, Dmitry Nikolaev, and Antanas Verikas. SPIE, 2018. http://dx.doi.org/10.1117/12.2310094.

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I. Mourikis, Anastasios, and Stergios I. Roumeliotis. "Performance Bounds for Cooperative Simultaneous Localization and Mapping (C-SLAM)." In Robotics: Science and Systems 2005. Robotics: Science and Systems Foundation, 2005. http://dx.doi.org/10.15607/rss.2005.i.010.

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Reports on the topic "Simultaneuos localization and mapping (SLAM)"

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Kelley, Troy D. Using a Cognitive Architecture to Solve Simultaneous Localization and Mapping (SLAM) Problems. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ad1016045.

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Kelley, Troy D. Using a Cognitive Architecture to Solve Simultaneous Localization and Mapping (SLAM) Problems. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada636872.

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Christie, Benjamin, Osama Ennasr, and Garry Glaspell. Autonomous navigation and mapping in a simulated environment. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42006.

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Unknown Environment Exploration (UEE) with an Unmanned Ground Vehicle (UGV) is extremely challenging. This report investigates a frontier exploration approach, in simulation, that leverages Simultaneous Localization And Mapping (SLAM) to efficiently explore unknown areas by finding navigable routes. The solution utilizes a diverse sensor payload that includes wheel encoders, three-dimensional (3-D) LIDAR, and Red, Green, Blue and Depth (RGBD) cameras. The main goal of this effort is to leverage frontier-based exploration with a UGV to produce a 3-D map (up to 10 cm resolution). The solution provided leverages the Robot Operating System (ROS).
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