12 research outputs found
Closing the Loop with Graphical SLAM
(c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Digital Object Identifier: 10.1109/TRO.2007.900608The problem of simultaneous localization and mapping
(SLAM) is addressed using a graphical method. The main
contributions are a computational complexity that scales well with
the size of the environment, the elimination of most of the linearization
inaccuracies, and a more flexible and robust data association.
We also present a detection criteria for closing loops.We show how
multiple topological constraints can be imposed on the graphical
solution by a process of coarse fitting followed by fine tuning. The
coarse fitting is performed using an approximate system. This approximate
system can be shown to possess all the local symmetries.
Observations made during the SLAM process often contain symmetries,
that is to say, directions of change to the state space that
do not affect the observed quantities. It is important that these
directions do not shift as we approximate the system by, for example,
linearization. The approximate system is both linear and
block diagonal. This makes it a very simple system to work with
especially when imposing global topological constraints on the solution.
These global constraints are nonlinear. We show how these
constraints can be discovered automatically.We develop a method
of testing multiple hypotheses for data matching using the graph.
This method is derived from statistical theory and only requires
simple counting of observations. The central insight is to examine
the probability of not observing the same features on a return to
a region. We present results with data from an outdoor scenario
using a SICK laser scanner
The simultaneous localization and mapping (SLAM):An overview
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
iSAM2 : incremental smoothing and mapping using the Bayes tree
Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of Sage for personal use, not for redistribution. The definitive version was published in International Journal of Robotics Research 31 (2012): 216-235, doi:10.1177/0278364911430419.We present a novel data structure, the Bayes tree, that provides an algorithmic foundation enabling a better understanding of
existing graphical model inference algorithms and their connection to sparse matrix factorization methods. Similar to a clique
tree, a Bayes tree encodes a factored probability density, but unlike the clique tree it is directed and maps more naturally to the
square root information matrix of the simultaneous localization and mapping (SLAM) problem. In this paper, we highlight three
insights provided by our new data structure. First, the Bayes tree provides a better understanding of the matrix factorization in
terms of probability densities. Second, we show how the fairly abstract updates to a matrix factorization translate to a simple
editing of the Bayes tree and its conditional densities. Third, we apply the Bayes tree to obtain a completely novel algorithm
for sparse nonlinear incremental optimization, named iSAM2, which achieves improvements in efficiency through incremental
variable re-ordering and fluid relinearization, eliminating the need for periodic batch steps. We analyze various properties of
iSAM2 in detail, and show on a range of real and simulated datasets that our algorithm compares favorably with other recent
mapping algorithms in both quality and efficiency.M. Kaess, H. Johannsson and J. Leonard were partially supported
by ONR grants N00014-06-1-0043 and N00014-10-1-0936. F. Dellaert and R. Roberts were partially supported by
NSF, award number 0713162, âRI: Inference in Large-Scale
Graphical Modelsâ. V. Ila has been partially supported by the
Spanish MICINN under the Programa Nacional de Movilidad
de Recursos Humanos de InvestigaciĂłn
An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation
The main focus of this paper is on extracting features with SOund Navigation And Ranging
(SONAR) sensing for further underwater landmark-based Simultaneous Localization and Mapping
(SLAM). According to the characteristics of sonar images, in this paper, an improved Otsu threshold
segmentation method (TSM) has been developed for feature detection. In combination with a
contour detection algorithm, the foreground objects, although presenting different feature shapes,
are separated much faster and more precisely than by other segmentation methods. Tests have
been made with side-scan sonar (SSS) and forward-looking sonar (FLS) images in comparison
with other four TSMs, namely the traditional Otsu method, the local TSM, the iterative TSM and
the maximum entropy TSM. For all the sonar images presented in this work, the computational
time of the improved Otsu TSM is much lower than that of the maximum entropy TSM, which
achieves the highest segmentation precision among the four above mentioned TSMs. As a result
of the segmentations, the centroids of the main extracted regions have been computed to represent
point landmarks which can be used for navigation, e.g., with the help of an Augmented Extended
Kalman Filter (AEKF)-based SLAM algorithm. The AEKF-SLAM approach is a recursive and iterative
estimation-update process, which besides a prediction and an update stage (as in classical Extended
Kalman Filter (EKF)), includes an augmentation stage. During navigation, the robot localizes the
centroids of different segments of features in sonar images, which are detected by our improved Otsu
TSM, as point landmarks. Using them with the AEKF achieves more accurate and robust estimations
of the robot pose and the landmark positions, than with those detected by the maximum entropy TSM.
Together with the landmarks identified by the proposed segmentation algorithm, the AEKF-SLAM
has achieved reliable detection of cycles in the map and consistent map update on loop closure, which
is shown in simulated experiments
Gestion de mémoire pour la détection de fermeture de boucle pour la cartographie temps réel par un robot mobile
Pour permettre Ă un robot autonome de faire des tĂąches complexes, il est important qu'il puisse cartographier son environnement pour s'y localiser. Ă long terme, pour corriger sa carte globale, il est nĂ©cessaire qu'il dĂ©tecte les endroits dĂ©jĂ visitĂ©s. C'est une des caractĂ©ristiques les plus importantes en localisation et cartographie simultanĂ©e (SLAM), mais aussi sa principale limitation. La charge de calcul augmente en fonction de la taille de l'environnement, et alors les algorithmes n'arrivent plus Ă s'exĂ©cuter en temps rĂ©el. Pour rĂ©soudre cette problĂ©matique, l'objectif est de dĂ©velopper un nouvel algorithme de dĂ©tection en temps rĂ©el d'endroits dĂ©jĂ visitĂ©s, et qui fonctionne peu importe la taille de l'environnement. La dĂ©tection de fermetures de boucle, c'est-Ă -dire la reconnaissance des endroits dĂ©jĂ visitĂ©s, est rĂ©alisĂ©e par un algorithme probabiliste robuste d'Ă©valuation de la similitude entre les images acquises par une camĂ©ra Ă intervalles rĂ©guliers. Pour gĂ©rer efficacement la charge de calcul de cet algorithme, la mĂ©moire du robot est divisĂ©e en mĂ©moires Ă long terme (base de donnĂ©es), Ă court terme et de travail (mĂ©moires vives). La mĂ©moire de travail garde les images les plus caractĂ©ristiques de l'environnement afin de respecter la contrainte d'exĂ©cution temps rĂ©el. Lorsque la contrainte de temps rĂ©el est atteinte, les images des endroits vus les moins souvent depuis longtemps sont transfĂ©rĂ©es de la mĂ©moire de travail Ă la mĂ©moire Ă long terme. Ces images transfĂ©rĂ©es peuvent ĂȘtre rĂ©cupĂ©rĂ©es de la mĂ©moire Ă long terme Ă la mĂ©moire de travail lorsqu'une image voisine dans la mĂ©moire de travail reçoit une haute probabilitĂ© que le robot soit dĂ©jĂ passĂ© par cet endroit, augmentant ainsi la capacitĂ© de dĂ©tecter des endroits dĂ©jĂ visitĂ©s avec les prochaines images acquises. Le systĂšme a Ă©tĂ© testĂ© avec des donnĂ©es prĂ©alablement prises sur le campus de l'UniversitĂ© de Sherbrooke afin d'Ă©valuer sa performance sur de longues distances, ainsi qu'avec quatre autres ensembles de donnĂ©es standards afin d'Ă©valuer sa capacitĂ© d'adaptation avec diffĂ©rents environnements. Les rĂ©sultats suggĂšrent que l'algorithme atteint les objectifs fixĂ©s et permet d'obtenir des performances supĂ©rieures que les approches existantes. Ce nouvel algorithme de dĂ©tection de fermeture de boucle peut ĂȘtre utilisĂ© directement comme une technique de SLAM topologique ou en parallĂšle avec une technique de SLAM existante afin de dĂ©tecter les endroits dĂ©jĂ visitĂ©s par un robot autonome. Lors d'une dĂ©tection de boucle, la carte globale peut alors ĂȘtre corrigĂ©e en utilisant la nouvelle contrainte crĂ©Ă©e entre le nouveau et l'ancien endroit semblable
Localização e mapeamento simultĂąneos em grandes ambientes : uma abordagem hĂbrida
Dissertação (mestrado)âUniversidade de BrasĂlia, Faculdade de Tecnologia, Departamento de Engenharia ElĂ©trica, 2012.Mapeamento e localização simultĂąneos (SLAM, da sigla em inglĂȘs) Ă© um dos assuntos mais pesquisados no campo da robĂłtica. Este trabalho propĂ”e uma abordagem de sistemas dinĂąmicos hĂbridos para tratar do problema de SLAM. Dentro desta perspectiva, desenvolve-se um modelomatemĂĄtico para o problema de localização e mapeamento em grandes ambientes e propĂ”e-se uma modificação no algoritmo do filtro hĂbrido IMM de forma a se alcançar um melhor desempenho durante o processo de estimação estocĂĄstica do vetor de estados do sistema. Este novo algoritmo, juntamente com a formulação mais tradicional FKE-SLAM e do filtro de partĂculas (FastSLAM), tem seu desempenho comparado por meio de dados de simulação. Sugere-se que a formulação apresentada neste trabalho pode superar os resultados disponĂveis na literatura em termos de complexidade computacional. ______________________________________________________________________________ ABSTRACTSimultaneous localization and mapping (the acronym, SLAM) is one of the most interesting topics in the field of robotics. This paper proposes a hybrid dynamic systems approach to address the problem of SLAM. Within this perspective, it develops a mathematical model to the problem of localization and mapping and propose a modification in the hybrid filter IMM algorithm in order to achieve better performance during the estimation process. This new algorithm, together with the formulation EKF-SLAM and FastSLAM, is compared by means of simulation data. It is suggested that the formulation presented in this paper can overcome the results available in the literature in terms of computational complexity
Algorithms and Methods for Received Signal Strength Based Wireless Localization
In the era of wireless communications, the demand for localization and localization-based services has been continuously growing, as increasingly smarter wireless devices have emerged to the market. Besides the already available satellite-based localization systems, such as the GPS and GLONASS, also other localization approaches are needed to complement the existing solutions. Finding different types of low-cost localization methods, especially for indoors, has become one of the most important research topics in recent years.One of the most used approaches in localization is based on Received Signal Strength (RSS) information. Specific fingerprints about RSS are collected and stored and positioning can be done through pattern or feature matching algorithms or through statistical inference. A great and immediate advantage of the RSS-based localization is its ability to exploit the already existing infrastructure of different communications networks without the need to install additional system hardware. Furthermore, due to the evident connection between the RSS level and the quality of a communications signal, the RSS is usually inherently included in the network measurements. This favors the availability of the RSS measurements in the current and future wireless communications systems.In this thesis, we study the suitability of RSS for localization in various communications systems including cellular networks, wireless local area networks, personal area networks, such as WiFi, Bluetooth and Radio Frequency Identification (RFID) tags. Based on substantial real-life measurement campaigns, we study different characteristics of RSS measurements and propose several Path Loss (PL) models to capture the essential behavior of the RSS levels in 2D outdoor and 3D indoor environments. By using the PL models, we show that it is possible to attain similar performance to fingerprinting with a database size of only 1-2% of the database size needed in fingerprinting. In addition, we study the effect of different error sources, such as database calibration errors, on the localization accuracy. Moreover, we propose a novel method for studying how coverage gaps in the fingerprint database affect the localization performance. Here, by using various interpolation and extrapolation methods, we improve the localization accuracy with imperfect fingerprint databases, such as those including substantial cover-age gaps due to inaccessible parts of the buildings
Long-term robot mapping in dynamic environments
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 139-144).One of the central goals in mobile robotics is to develop a mobile robot that can construct a map of an initially unknown dynamic environment. This is often referred to as the Simultaneous Localization and Mapping (SLAM) problem. A number of approaches to the SLAM problem have been successfully developed and applied, particularly to a mobile robot constructing a map of a 2D static indoor environment. While these methods work well for static environments, they are not robust to dynamic environments which are complex and composed of numerous objects that move at wide-varying time-scales, such as people or office furniture. The problem of maintaining a map of a dynamic environment is important for both real-world applications and for the advancement of robotics. A mobile robot executing extended missions, such as autonomously collecting data underwater for months or years, must be able to reliably know where it is, update its map as the environment changes, and recover from mistakes. From a fundamental perspective, this work is important in order to understand and determine the problems that occur with existing mapping techniques for persistent long-term operation. The primary contribution of the thesis is Dynamic Pose Graph SLAM (DPG-SLAM), a novel algorithm that addresses two core challenges of the long-term mapping problem. The first challenge is to ensure that the robot is able to remain localized in a changing environment over great lengths of time. The second challenge is to be able to maintain an up-to-date map over time in a computationally efficient manner. DPG-SLAM directly addresses both of these issues to enable long-term mobile robot navigation and map maintenance in changing environments. Using Kaess and Dellaert's incremental Smoothing and Mapping (iSAM) as the underlying SLAM state estimation engine, the dynamic pose graph evolves over time as the robot explores new areas and revisits previously mapped areas. The algorithm is demonstrated on two real-world dynamic indoor laser data sets, demonstrating the ability to maintain an efficient, up-to-date map despite long-term environmental changes. Future research issues, such as the integration of adaptive exploration with dynamic map maintenance, are identified.by Aisha Naima Walcott.Ph.D
Robust and efficient robotic mapping
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 123-129).Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while large-scale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots. Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and non-linearities make the problem even more difficult especially when using inexpensive (and therefore preferable) sensors. This thesis describes an optimization algorithm that can rapidly estimate the maximum likelihood map given a set of observations. The algorithm, which iteratively reduces map error by considering a single observation at a time, scales well to large environments with many observations. The approach is particularly robust to noise and non-linearities, quickly escaping local minima that trap current methods. Both batch and online versions of the algorithm are described. In order to build a map, however, a robot must first be able to recognize places that it has previously seen. Limitations in sensor processing algorithms, coupled with environmental ambiguity, make this difficult. Incorrect place recognitions can rapidly lead to divergence of the map. This thesis describes a place recognition algorithm that can robustly handle ambiguous data. We evaluate these algorithms on a number of challenging datasets and provide quantitative comparisons to other state-of-the-art methods, illustrating the advantages of our methods.by Edwin B. Olson.Ph.D
Cartographie, localisation et planification simultaneÌes âen ligneâ, aÌ long terme et aÌ grande eÌchelle pour robot mobile
Pour eÌtre en mesure de naviguer dans des endroits inconnus et non structureÌs, un robot doit pouvoir cartographier lâenvironnement afin de sây localiser. Ce probleÌme est connu sous le nom de cartographie et localisation simultaneÌes (ou SLAM pour Simultaneous Localization and Mapping). Une fois la carte de lâenvironnement creÌeÌe, des taÌches requeÌrant un deÌplacement dâun endroit connu aÌ un autre peuvent ainsi eÌtre planifieÌes. La charge de calcul du SLAM est deÌpendante de la grandeur de la carte. Un robot a une puissance de calcul embarqueÌe limiteÌe pour arriver aÌ traiter lâinformation âen ligneâ, câest-aÌ-dire aÌ bord du robot avec un temps de traitement des donneÌes moins long que le temps dâacquisition des donneÌes ou le temps maximal permis de mise aÌ jour de la carte. La navigation du robot tout en faisant le SLAM est donc limiteÌe par la taille de lâenvironnement aÌ cartographier.
Pour reÌsoudre cette probleÌmatique, lâobjectif est de deÌvelopper un algorithme de SPLAM (Simultaneous Planning Localization and Mapping) permettant la navigation peu importe la taille de lâenvironment. Pour geÌrer efficacement la charge de calcul de cet algorithme, la meÌmoire du robot est diviseÌe en une meÌmoire de travail et une meÌmoire aÌ long terme. Lorsque la contrainte de traitement âen ligneâ est atteinte, les endroits vus les moins souvent et qui ne sont pas utiles pour la navigation sont transfeÌreÌes de la meÌmoire de travail aÌ la meÌmoire aÌ long terme. Les endroits transfeÌreÌs dans la meÌmoire aÌ long terme ne sont plus utiliseÌs pour la navigation. Cependant, ces endroits transfeÌreÌs peuvent eÌtre reÌcupeÌreÌes de la meÌmoire aÌ long terme aÌ la meÌmoire de travail lorsque le le robot sâapproche dâun endroit voisin encore dans la meÌmoire de travail. Le robot peut ainsi se rappeler increÌmentalement dâune partie de lâenvironment a priori oublieÌe afin de pouvoir sây localiser pour le suivi de trajectoire.
Lâalgorithme, nommeÌ RTAB-Map, a eÌteÌ testeÌ sur le robot AZIMUT-3 dans une premieÌre expeÌrience de cartographie sur cinq sessions indeÌpendantes, afin dâeÌvaluer la capaciteÌ du systeÌme aÌ fusionner plusieurs cartes âen ligneâ. La seconde expeÌrience, avec le meÌme robot utiliseÌ lors de onze sessions totalisant 8 heures de deÌplacement, a permis dâeÌvaluer la capaciteÌ du robot de naviguer de façon autonome tout en faisant du SLAM et planifier des trajectoires continuellement sur une longue peÌriode en respectant la contrainte de traitement âen ligneâ . Enfin, RTAB-Map est compareÌ aÌ dâautres systeÌmes de SLAM sur quatre ensembles de donneÌes populaires pour des applications de voiture autonome (KITTI), balayage aÌ la main avec une cameÌra RGB-D (TUM RGB-D), de drone (EuRoC) et de navigation inteÌrieur avec un robot PR2 (MIT Stata Center).
Les reÌsultats montrent que RTAB-Map peut eÌtre utiliseÌ sur de longue peÌriode de temps en navigation autonome tout en respectant la contrainte de traitement âen ligneâ et avec une qualiteÌ de carte comparable aux approches de lâeÌtat de lâart en SLAM visuel et avec teÌleÌmeÌtre laser. ll en reÌsulte dâun logiciel libre deÌployeÌ dans une multitude dâapplications allant des robots mobiles inteÌrieurs peu couÌteux aux voitures autonomes, en passant par les drones et la modeÌlisation 3D de lâinteÌrieur dâune maison