Relevant bibliographies by topics / Graph SLAM

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Graph SLAM'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Graph SLAM"

1

Li, Jin Liang, Ji Hua Bao, and Yan Yu. "Graph SLAM for Rescue Robots." Applied Mechanics and Materials 433-435 (October 2013): 134–37. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.134.

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This paper studied the mapping problem for rescue robots. Being able to build a map of the rescue environment and simultaneously localize within this map is an essential skill for rescue robots. We formulate the SLAM problem as a graph whose nodes correspond to the poses of the robot at different points and whose edges represent constraints between poses. Optimizing large pose graphs has been a bottleneck for mobile robots, since the computation time of direct nonlinear optimization can grow rapidly with the size of graph. In this paper, we propose an efficient method for constructing and solving a linear problem. We demonstrate its effectiveness on a large set of real-world maps.
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Khosoussi, Kasra, Matthew Giamou, Gaurav S. Sukhatme, Shoudong Huang, Gamini Dissanayake, and Jonathan P. How. "Reliable Graphs for SLAM." International Journal of Robotics Research 38, no. 2-3 (January 22, 2019): 260–98. http://dx.doi.org/10.1177/0278364918823086.

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Estimation-over-graphs (EoG) is a class of estimation problems that admit a natural graphical representation. Several key problems in robotics and sensor networks, including sensor network localization, synchronization over a group, and simultaneous localization and mapping (SLAM) fall into this category. We pursue two main goals in this work. First, we aim to characterize the impact of the graphical structure of SLAM and related problems on estimation reliability. We draw connections between several notions of graph connectivity and various properties of the underlying estimation problem. In particular, we establish results on the impact of the weighted number of spanning trees on the D-optimality criterion in 2D SLAM. These results enable agents to evaluate estimation reliability based only on the graphical representation of the EoG problem. We then use our findings and study the problem of designing sparse SLAM problems that lead to reliable maximum likelihood estimates through the synthesis of sparse graphs with the maximum weighted tree connectivity. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, we establish several new theoretical results, including the monotone log-submodularity of the weighted number of spanning trees. We exploit these structures and design a complementary greedy–convex pair of efficient approximation algorithms with provable guarantees. The proposed synthesis framework is applied to various forms of the measurement selection problem in resource-constrained SLAM. Our algorithms and theoretical findings are validated using random graphs, existing and new synthetic SLAM benchmarks, and publicly available real pose-graph SLAM datasets.
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LIANG, Mingjie, Huaqing MIN, and Ronghua LUO. "Graph-based SLAM: A Survey." Robot 35, no. 4 (2013): 500. http://dx.doi.org/10.3724/sp.j.1218.2013.00500.

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IRIE, Kiyoshi, and Masahiro TOMONO. "A Compact and Portable Implementation of Graph-based SLAM." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 2P2—B01. http://dx.doi.org/10.1299/jsmermd.2017.2p2-b01.

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Liu, Tian, Yongfu Chen, Zhiyong Jin, Kai Li, Zhenting Wang, and Jiongzhi Zheng. "Spare Pose Graph Decomposition and Optimization for SLAM." MATEC Web of Conferences 256 (2019): 05003. http://dx.doi.org/10.1051/matecconf/201925605003.

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The graph optimization has become the mainstream technology to solve the problems of SLAM (simultaneous localization and mapping). The pose graph in the graph based SLAM is consisted with a series of nodes and edges that connect the adjacent or related poses. With the widespread use of mobile robots, the scale of pose graph has rapidly increased. Therefore, optimizing a large-scale pose graph is the bottleneck of application of graph based SLAM. In this paper, we propose an optimization method basing on the decomposition of pose graph, of which we have noticed the sparsity. With the extraction of the Single-chain and the Parallel-chain, the pose graph is decomposed into many small subgraphs. Compared with directly processing the original graph, the speed of calculation is accelerated by separately optimizing the subgraph, which is because the computational complexity is increasing exponentially with the increase of the graph’s scale. This method we proposed is very suitable for the current multi-threaded framework adopted in the mainstream SLAM, which separately calculate the subgraph decomposed by our method, rather than the original optimization requiring a large block of time in once may cause CPU obstruction. At the end of the paper, our algorithm is validated with the open source dataset of the mobile robot, of which the result illustrates our algorithm can reduce the one-time resource consumption and the time consumption of the calculation with the same map-constructing accuracy.
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Taguchi, Shun, Hideki Deguchi, Noriaki Hirose, and Kiyosumi Kidono. "Fast Bayesian graph update for SLAM." Advanced Robotics 36, no. 7 (January 28, 2022): 333–43. http://dx.doi.org/10.1080/01691864.2021.2013939.

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Grisetti, G., R. Kummerle, C. Stachniss, and W. Burgard. "A Tutorial on Graph-Based SLAM." IEEE Intelligent Transportation Systems Magazine 2, no. 4 (2010): 31–43. http://dx.doi.org/10.1109/mits.2010.939925.

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Cheng, Jiantong, Jonghyuk Kim, Jinliang Shao, and Weihua Zhang. "Robust linear pose graph-based SLAM." Robotics and Autonomous Systems 72 (October 2015): 71–82. http://dx.doi.org/10.1016/j.robot.2015.04.010.

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Chen, Shoubin, Baoding Zhou, Changhui Jiang, Weixing Xue, and Qingquan Li. "A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization." Remote Sensing 13, no. 14 (July 10, 2021): 2720. http://dx.doi.org/10.3390/rs13142720.

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LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author.
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Akpınar, Burak. "Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications." Applied System Innovation 4, no. 4 (December 17, 2021): 101. http://dx.doi.org/10.3390/asi4040101.

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Indoor and outdoor mapping studies can be completed relatively quickly, depending on the developments in Mobile Mapping Systems. Especially in indoor environments where high accuracy GNSS positions cannot be used, mapping studies can be carried out with SLAM algorithms. Although there are many different SLAM algorithms in the literature, each can produce results with different accuracy according to the mapped environment. In this study, 3D maps were produced with LOAM, A-LOAM, and HDL Graph SLAM algorithms in different environments such as long corridors, staircases, and outdoor environments, and the accuracies of the maps produced with different algorithms were compared. For this purpose, a mobile mapping platform using Velodyne VLP-16 LIDAR sensor was developed, and the odometer drift, which causes loss of accuracy in the data collected, was minimized by loop closure and plane detection methods. As a result of the tests, it was determined that the results of the LOAM algorithm were not as accurate as those of the A-LOAM and HDL Graph SLAM algorithms. Both indoor and outdoor environments and the A-LOAM results’ accuracy were two times better than HDL Graph SLAM results.
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Dissertations / Theses on the topic "Graph SLAM"

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Victorin, Henning. "COMPARISON OF THE GRAPH-OPTIMIZATION FRAMEWORKS G2O AND SBA." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-31764.

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This thesis starts with an introduction to Simulataneous Localization and Mapping (SLAM) and more background on Visual SLAM (VSLAM). The goal of VSLAM is to map the world with a camera, and at the same time localize the camera in that world. One important step is to optimize the acquired map, which can be done in several different ways. In this thesis, two state-of-the-art optimization algorithms are identified and compared, namely the g2o package and the SBA package. The results show that SBA is better on smaller datasets, and g2o on larger. It is also discovered that there is an error in the implementation of the pinhole camera model in the SBA package.
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Natarajan, Ramkumar. "Efficient Factor Graph Fusion for Multi-robot Mapping." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-theses/1201.

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"This work presents a novel method to efficiently factorize the combination of multiple factor graphs having common variables of estimation. The fast-paced innovation in the algebraic graph theory has enabled new tools of state estimation like factor graphs. Recent factor graph formulation for Simultaneous Localization and Mapping (SLAM) like Incremental Smoothing and Mapping using the Bayes tree (ISAM2) has been very successful and garnered much attention. Variable ordering, a well-known technique in linear algebra is employed for solving the factor graph. Our primary contribution in this work is to reuse the variable ordering of the graphs being combined to find the ordering of the fused graph. In the case of mapping, multiple robots provide a great advantage over single robot by providing a faster map coverage and better estimation quality. This coupled with an inevitable increase in the number of robots around us produce a demand for faster algorithms. For example, a city full of self-driving cars could pool their observation measurements rapidly to plan a traffic free navigation. By reusing the variable ordering of the parent graphs we were able to produce an order-of-magnitude difference in the time required for solving the fused graph. We also provide a formal verification to show that the proposed strategy does not violate any of the relevant standards. A common problem in multi-robot SLAM is relative pose graph initialization to produce a globally consistent map. The other contribution addresses this by minimizing a specially formulated error function as a part of solving the factor graph. The performance is illustrated on a publicly available SuiteSparse dataset and the multi-robot AP Hill dataset."
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Concha, Edison Kleiber Titito. "Map point optimization in keyframe-based SLAM using covisibbility graph and information fusion." Universidade Federal do Rio Grande do Sul, 2018. http://hdl.handle.net/10183/180265.

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SLAM (do inglês Simultaneous Localization and Mapping) Monocular baseado em Keyframes é uma das principais abordagens de SLAM Visuais, usado para estimar o movimento da câmera juntamente com a reconstrução do mapa sobre frames selecionados. Estas técnicas representam o ambiente por pontos no mapa localizados em um espaço tri-dimensional, que podem ser reconhecidos e localizados no frame. Contudo, estas técnicas não podem decidir quando um ponto do mapa se torna um outlier ou uma informação obsoleta e que pode ser descartada, ou combinar pontos do mapa que correspondem ao mesmo ponto tri-dimensional. Neste trabalho, apresentamos um método robusto para manter um mapa refinado. Esta abordagem usa o grafo de covisibilidade e um algoritmo baseado na fusão de informações para construir um mapa probabilístico, que explicitamente modela medidas de outlier. Além disso, incorporamos um mecanismo de poda para reduzir informações redundantes e remover outliers. Desta forma, nossa abordagem gerencia a redução do tamanho do mapa, mantendo informações essenciais do ambiente. Finalmente, a fim de avaliar a performance do nosso método, ele foi incorporado ao sistema do ORB-SLAM e foi medido a acurácia alcançada em datasets publicamente disponíveis que contêm sequências de imagens de ambientes internos gravados com uma câmera monocular de mão.
Keyframe-based monocular SLAM (Simultaneous Localization and Mapping) is one of the main visual SLAM approaches, used to estimate the camera motion together with the map reconstruction over selected frames. These techniques based on keyframes represent the environment by map points located in the three-dimensional space that can be recognized and located in the frames. However, many of these techniques cannot combine map points corresponding to the same three-dimensional point or detect when a map point becomes outlier and an obsolete information. In this work, we present a robust method to maintain a refined map that uses the covisibility graph and an algorithm based on information fusion to build a probabilistic map, which explicitly models outlier measurements. In addition, we incorporate a pruning mechanism to reduce redundant information and remove outliers. In this way our approach manages the map size maintaining essential information of the environment. Finally, in order to evaluate the performance of our method, we incorporate it into an ORB-SLAM system and measure the accuracy achieved on publicly available benchmark datasets which contain indoor images sequences recorded with a hand-held monocular camera.
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Schabert, Antek. "Integrating the use of prior information into Graph-SLAM with NDTregistration for loop detection." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-61379.

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Marburg, Aaron Ming. "Towards persistent navigation with a downward-looking camera." Thesis, University of Canterbury. Department of Electrical and Computer Engineering, 2015. http://hdl.handle.net/10092/10421.

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This research focuses on the development of a persistent navigation algorithm for a hovering vehicle with a single, downward-facing visible spectrum camera. A successful persistent navigation algorithm allows a vehicle to: * Continuously estimate its location and pose within a local, if not global, coordinate frame. * Continuously align incoming data to both temporally proximal and temporally distant data. For aerial images, this alignment is equivalent to image mosaicking, as is commonly used in aerial photogrammetry to produce broad-scale photomaps from a sequence of discrete images. * Operate relative to, and be commanded relative to the sensor data, rather than relative to an abstract coordinate system. The core application space considered here is moderate-to-high altitude aerial mapping, and a number of sets of high-resolution, high-overlap aerial photographs are used as the core test data set. These images are captured from a sufficient altitude that the apparent perspective shift of objects on the ground is minimized -- the scene is effectively planar. As such, this research focuses heavily on the properties and advantages available when processing such planar images. This research is split into two threads which track the two main challenges in visual persistent navigation: the association and alignment of visual data given significant image change, and the development of an estimation algorithm and data storage structure with bounded computational and storage costs for a fixed map size. Persistent navigation requires the robot to accurately align incoming images against historical data. By its nature, however, visual data contains a high degree of variability despite minimal changes in the scene itself. As a simple example, as the sun moves and weather conditions change, the apparent illumination and shading of objects in the scene can vary significantly. More critically, image alignment must be robust to change in the scene itself, as that change is often a critical output from the robot's re-exploration. This problem is considered in two contexts. First, a set of state-of-the-art feature detection algorithms are evaluated against sample data sets which include both temporally proximal and disparate images of the same location. The capacity of each algorithm to identify repeated point features is measured for a spectrum of algorithm-specific parameter values. Next, the potential of using a prior estimate on the inter-image geometry to improve the robustness of precise image alignment is considered for two phases of the image alignment process: feature matching and robust outlier rejection. A number of geometry-aware algorithms are proposed for both phases, and tested against similar sets of similar and disparate aerial images. While many of the proposed algorithms do improve on the performance of the unguided algorithms, none are vastly superior. The second thread starts by considering the problem of navigation fromdownward-looking aerial images from the perspective of Simultaneous Localization and Mapping (SLAM). This leads to the development of Simultaneous Mosaicking and Resectioning Through Planar Image Graphs (SMARTPIG), an online, iterative mosaicking and SLAM algorithm built on the assumption of a planar scene. A number of samples of SMARTPIG outputs are shown, including mosaics of a 600-meter square airport with approximately 3-meter reprojection errors relative to ground control points. SMARTPIG, like most SLAM algorithms, does not fulfill the criteria for persistent navigation because the computational and storage costs are proportional to the total mission length, not the total area explored. SMARTPIG is evolved towards persistent navigation by the introduction of the featurescape, a storage structure for long-term point-feature data, to produce Planar Image Graphs for PErsistent Navigation (PIGPEN). PIGPEN is demonstrated perfoming robot re-localization onto an existing SMARTPIG mosaic with an accuracy comparable to the original mosaic.
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Relfsson, Emil. "Map Partition and Loop Closure in a Factor Graph Based SAM System." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171894.

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The graph-based formulation of the navigation problem is establishing itself as one of the standard ways to formulate the navigation problem within the sensor fusion community. It enables a convenient way to access information from previous positions which can be used to enhance the estimate of the current position.To restrict working memory usage, map partitioning can be used to store older parts of the map on a hard drive, in the form of submaps. This limits the number of previous positions within the active map. This thesis examines the effect that map partitioning information loss has on the state of the art positioning algorithm iSAM2, both in open routes and when loop closure is achieved. It finds that larger submaps appear to cause a smaller positional error than smaller submaps for open routes. The smaller submaps seem to give smaller positional error than larger submaps when loop closure is achieved. The thesis also examines how the density of landmarks at the partition point affects the positional error, but the obtained result is mixed and no clear conclusions can be made. Finally it reviews some loop closure detection algorithms that can be convenient to pair with the iSAM2 algorithm.
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Murphy, Timothy Charles. "Examining the Effects of Key Point Detector and Descriptors on 3D Visual SLAM." Ohio University Honors Tutorial College / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1461320700.

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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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Lange, Sven. "Faktorgraph-basierte Sensordatenfusion zur Anwendung auf einem Quadrocopter." Doctoral thesis, Universitätsbibliothek Chemnitz, 2013. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-130576.

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Die Sensordatenfusion ist eine allgegenwärtige Aufgabe im Bereich der mobilen Robotik und darüber hinaus. In der vorliegenden Arbeit wird das typischerweise verwendete Verfahren zur Sensordatenfusion in der Robotik in Frage gestellt und anhand von neuartigen Algorithmen, basierend auf einem Faktorgraphen, gelöst sowie mit einer korrespondierenden Extended-Kalman-Filter-Implementierung verglichen. Im Mittelpunkt steht dabei das technische sowie algorithmische Sensorkonzept für die Navigation eines Flugroboters im Innenbereich. Ausführliche Experimente zeigen die Qualitätssteigerung unter Verwendung der neuen Variante der Sensordatenfusion, aber auch Einschränkungen und Beispiele mit nahezu identischen Ergebnissen beider Varianten der Sensordatenfusion. Neben Experimenten anhand einer hardwarenahen Simulation wird die Funktionsweise auch anhand von realen Hardwaredaten evaluiert.
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Sünderhauf, Niko. "Robust optimization for simultaneous localization and mapping." Thesis, Technischen Universitat Chemnitz, 2012. https://eprints.qut.edu.au/109667/1/109667.pdf.

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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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Books on the topic "Graph SLAM"

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Nash, G. F. J. Bridges to BS5400: Tables and graphs for simply supported beam and slab design. Croydon: Constrado, 1985.

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Sports, Yeoys. Nothing but Net Swish: Cool Basketball Player Journal for Coaches, Streetball, Competition and Slam Dunk Fans - 6x9 - 100 Blank Graph Paper Pages. Independently Published, 2019.

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McIntyre, Allison. Slay All Day - Trendy Red Lips Print Journal (Graph). Independently Published, 2021.

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Creations, Rengaw. Slay Girl Slay Composition Notebook - 4x4 Quad Ruled: 7.44 x 9.69 - 200 Pages - Graph Paper - School Student Teacher Office. CreateSpace Independent Publishing Platform, 2018.

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Publishing, Frank H. I Didn't Come to Play, I Came to Slay- Notebook 120 Pages : 6''x 9'' Checkerd Graph Paper: Perfekt As a Log Notebook, Diarys, Day Planner, Journal and to-Do List for Work, University or at School. Independently Published, 2021.

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Book chapters on the topic "Graph SLAM"

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Placed, Julio A., Juan J. Gómez Rodríguez, Juan D. Tardós, and José A. Castellanos. "ExplORB-SLAM: Active Visual SLAM Exploiting the Pose-graph Topology." In ROBOT2022: Fifth Iberian Robotics Conference, 199–210. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21065-5_17.

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Chaves, Stephen M., Enric Galceran, Paul Ozog, Jeffrey M. Walls, and Ryan M. Eustice. "Pose-Graph SLAM for Underwater Navigation." In Sensing and Control for Autonomous Vehicles, 143–60. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55372-6_7.

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Khosoussi, Kasra, Gaurav S. Sukhatme, Shoudong Huang, and Gamini Dissanayake. "Designing Sparse Reliable Pose-Graph SLAM: A Graph-Theoretic Approach." In Springer Proceedings in Advanced Robotics, 17–32. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43089-4_2.

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Carrasco, Pep Lluis Negre, Francisco Bonin-Font, and Gabriel Oliver Codina. "Stereo Graph-SLAM for Autonomous Underwater Vehicles." In Intelligent Autonomous Systems 13, 351–60. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08338-4_26.

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Stachniss, Cyrill, and Henrik Kretzschmar. "Pose Graph Compression for Laser-Based SLAM." In Springer Tracts in Advanced Robotics, 271–87. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29363-9_16.

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Jung, Jongdae, Jinwoo Choi, Taekjun Oh, and Hyun Myung. "Indoor Magnetic Pose Graph SLAM with Robust Back-End." In Robot Intelligence Technology and Applications 5, 153–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78452-6_14.

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Zhang, He, Zifeng Hou, Nanjun Li, and Shuang Song. "A Graph-Based Hierarchical SLAM Framework for Large-Scale Mapping." In Intelligent Robotics and Applications, 439–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33515-0_44.

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Chen, Chunxu, Ling Pei, Changqing Xu, Danping Zou, Yuhui Qi, Yifan Zhu, and Tao Li. "Trajectory Optimization of LiDAR SLAM Based on Local Pose Graph." In Lecture Notes in Electrical Engineering, 360–70. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7751-8_36.

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Romero, Anna, and Miguel Cazorla. "Topological SLAM Using Omnidirectional Images: Merging Feature Detectors and Graph-Matching." In Advanced Concepts for Intelligent Vision Systems, 464–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17688-3_43.

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Park, Byeolteo, and Hyun Myung. "Directional Drilling Localization Using Graph SLAM and Magnetic Field in Backward Travel." In Advances in Intelligent Systems and Computing, 3–9. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16841-8_1.

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Conference papers on the topic "Graph SLAM"

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Ruifang, Dong, Vincent Fremont, Simon Lacroix, Isabelle Fantoni, and Liu Changan. "Line-based monocular graph SLAM." In 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). IEEE, 2017. http://dx.doi.org/10.1109/mfi.2017.8170369.

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Mazuran, Mladen, Tipaldi Gian Diego, Spinello Luciano, and Wolfram Burgard. "Nonlinear Graph Sparsification for SLAM." In Robotics: Science and Systems 2014. Robotics: Science and Systems Foundation, 2014. http://dx.doi.org/10.15607/rss.2014.x.040.

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Mendes, Ellon, Pierrick Koch, and Simon Lacroix. "ICP-based pose-graph SLAM." In 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). IEEE, 2016. http://dx.doi.org/10.1109/ssrr.2016.7784298.

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Liang, Zhiwei, Xiaogen Xu, and Zhenzhen Fu. "A visual SLAM using graph method." In 2014 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2014. http://dx.doi.org/10.1109/icma.2014.6885787.

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Reymann, Christophe, and Simon Lacroix. "Learning error models for graph SLAM." In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. http://dx.doi.org/10.1109/icra40945.2020.9196864.

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Rogers, John G., and Henrik I. Christensen. "Normalized graph cuts for visual SLAM." In 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009). IEEE, 2009. http://dx.doi.org/10.1109/iros.2009.5354269.

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Eade, E., P. Fong, and M. E. Munich. "Monocular graph SLAM with complexity reduction." In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5649205.

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Meng, Xianwei, Bonian Li, Bonian Li, Bonian Li, and Bonian Li. "PROB-SLAM: Real-time Visual SLAM Based on Probabilistic Graph Optimization." In ICRAI 2022: 2022 8th International Conference on Robotics and Artificial Intelligence. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3573910.3573920.

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Yoo, Wonsok, Hanjun Kim, Hyunki Hong, and Beom H. Lee. "Scan Similarity-based Pose Graph Construction method for Graph SLAM." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593605.

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Whelan, Thomas, Stefan Leutenegger, Renato Salas Moreno, Ben Glocker, and Andrew Davison. "ElasticFusion: Dense SLAM Without A Pose Graph." In Robotics: Science and Systems 2015. Robotics: Science and Systems Foundation, 2015. http://dx.doi.org/10.15607/rss.2015.xi.001.

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Reports on the topic "Graph SLAM"

1

Johannsson, Hordur, Michael Kaess, Marice Fallon, and John J. Leonard. Temporally Scalable Visual SLAM using a Reduced Pose Graph. Fort Belvoir, VA: Defense Technical Information Center, May 2012. http://dx.doi.org/10.21236/ada576491.

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