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Academic literature on the topic 'Graph-based localization and mapping'

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Published: 14 March 2023

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Journal articles on the topic "Graph-based localization and mapping"

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Xiong, Hui, Youping Chen, Xiaoping Li, and Bing Chen. "A two-level optimized graph-based simultaneous localization and mapping algorithm." Industrial Robot: An International Journal 45, no. 6 (October 15, 2018): 758–65. http://dx.doi.org/10.1108/ir-04-2018-0078.

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PurposeBecause submaps including a subset of the global map contain more environmental information, submap-based graph simultaneous localization and mapping (SLAM) has been studied by many researchers. In most of those studies, helpful environmental information was not taken into consideration when designed the termination criterion of the submap construction process. After optimizing the graph, cumulative error within the submaps was also ignored. To address those problems, this paper aims to propose a two-level optimized graph-based SLAM algorithm.Design/methodology/approachSubmaps are updated by extended Kalman filter SLAM while no geometric-shaped landmark models are needed; raw laser scans are treated as landmarks. A more reasonable criterion called the uncertainty index is proposed to combine with the size of the submap to terminate the submap construction process. After a submap is completed and a loop closure is found, a two-level optimization process is performed to minimize the loop closure error and the accumulated error within the submaps.FindingsSimulation and experimental results indicate that the estimated error of the proposed algorithm is small, and the maps generated are consistent whether in global or local.Practical implicationsThe proposed method is robust to sparse pedestrians and can be adapted to most indoor environments.Originality/valueIn this paper, a two-level optimized graph-based SLAM algorithm is proposed.
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Xu, Hao, Huafei Sun, Yongqiang Cheng, and Hao Liu. "Wireless sensor networks localization based on graph embedding with polynomial mapping." Computer Networks 106 (September 2016): 151–60. http://dx.doi.org/10.1016/j.comnet.2016.06.032.

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Zhu, Zihan, Yi Zhang, Weijun Wang, Wei Feng, Haowen Luo, and Yaojie Zhang. "Adaptive Adjustment of Factor’s Weight for a Multi-Sensor SLAM." Journal of Physics: Conference Series 2451, no. 1 (March 1, 2023): 012004. http://dx.doi.org/10.1088/1742-6596/2451/1/012004.

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Abstract A multi-sensor fusion simultaneous localization and mapping(SLAM) method based on factor graph optimization that can adaptively modify the weight of the graph factor is proposed in this study, to enhance the localization and mapping capability of autonomous robots in tough situations. Firstly, the algorithm fuses multi-lines lidar, monocular camera, and inertial measurement unit(IMU) in the odometry. Second, the factor graph is constructed using lidar and visual odometry as the unary edge and binary edge constraints, respectively, with the motion determined by IMU odometry serving as the primary odometry in the system. Finally, different increments of IMU odometry, lidar odometry and visual odometry are computed as favor factors to realize the adaptive adjustment of the factor’s weight. The suggested method has greater location accuracy and a better mapping effect in complex situations when compared to previous algorithms.
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Mukherjee, Shohin, Michael Kaess, Joseph N. Martel, and Cameron N. Riviere. "EyeSAM: graph-based localization and mapping of retinal vasculature during intraocular microsurgery." International Journal of Computer Assisted Radiology and Surgery 14, no. 5 (February 21, 2019): 819–28. http://dx.doi.org/10.1007/s11548-019-01925-1.

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Ren, Zhuli, Liguan Wang, and Lin Bi. "Robust GICP-Based 3D LiDAR SLAM for Underground Mining Environment." Sensors 19, no. 13 (July 1, 2019): 2915. http://dx.doi.org/10.3390/s19132915.

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Unmanned mining is one of the most effective methods to solve mine safety and low efficiency. However, it is the key to accurate localization and mapping for underground mining environment. A novel graph simultaneous localization and mapping (SLAM) optimization method is proposed, which is based on Generalized Iterative Closest Point (GICP) three-dimensional (3D) point cloud registration between consecutive frames, between consecutive key frames and between loop frames, and is constrained by roadway plane and loop. GICP-based 3D point cloud registration between consecutive frames and consecutive key frames is first combined to optimize laser odometer constraints without other sensors such as inertial measurement unit (IMU). According to the characteristics of the roadway, the innovative extraction of the roadway plane as the node constraint of pose graph SLAM, in addition to automatic removing the noise point cloud to further improve the consistency of the underground roadway map. A lightweight and efficient loop detection and optimization based on rules and GICP is designed. Finally, the proposed method was evaluated in four scenes (such as the underground mine laboratory), and compared with the existing 3D laser SLAM method (such as Lidar Odometry and Mapping (LOAM)). The results show that the algorithm could realize low drift localization and point cloud map construction. This method provides technical support for localization and navigation of underground mining environment.
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Dai, Kai, Bohua Sun, Guanpu Wu, Shuai Zhao, Fangwu Ma, Yufei Zhang, and Jian Wu. "LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios." Journal of Imaging 9, no. 2 (February 20, 2023): 52. http://dx.doi.org/10.3390/jimaging9020052.

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LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5–10 cm mapping accuracy and 20–30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.
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Zhang Tianxi, 张天喜, 周军 Zhou Jun, 廖华丽 Liao Huali, and 杨跟 Yang Gen. "Simultaneous Localization and Mapping Strategy of Graph Optimization Based on Three-Dimensional Laser." Laser & Optoelectronics Progress 56, no. 20 (2019): 201502. http://dx.doi.org/10.3788/lop56.201502.

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Wu, Xinzhao, Peiqing Li, Qipeng Li, and Zhuoran Li. "Two-dimensional-simultaneous Localisation and Mapping Study Based on Factor Graph Elimination Optimisation." Sustainability 15, no. 2 (January 8, 2023): 1172. http://dx.doi.org/10.3390/su15021172.

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A robust multi-sensor fusion simultaneous localization and mapping (SLAM) algorithm for complex road surfaces is proposed to improve recognition accuracy and reduce system memory occupation, aiming to enhance the computational efficiency of light detection and ranging in complex environments. First, a weighted signed distance function (W-SDF) map-based SLAM method is proposed. It uses a W-SDF map to capture the environment with less accuracy than the raster size but with high localization accuracy. The Levenberg–Marquardt method is used to solve the scan-matching problem in laser SLAM; it effectively alleviates the limitations of the Gaussian–Newton method that may lead to insufficient local accuracy, and reduces localisation errors. Second, ground constraint factors are added to the factor graph, and a multi-sensor fusion localisation algorithm is proposed based on factor graph elimination optimisation. A sliding window is added to the chain factor graph model to retain the historical state information within the window and avoid high-dimensional matrix operations. An elimination algorithm is introduced to transform the factor graph into a Bayesian network to marginalize the historical states and reduce the matrix dimensionality, thereby improving the algorithm localisation accuracy and reducing the memory occupation. Finally, the proposed algorithm is compared and validated with two traditional algorithms based on an unmanned cart. Experiments show that the proposed algorithm reduces memory consumption and improves localisation accuracy compared to the Hector algorithm and Cartographer algorithm, has good performance in terms of accuracy, reliability and computational efficiency in complex pavement environments, and is better utilised in practical environments.
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Xu, Shaoyan, Tao Wang, Congyan Lang, Songhe Feng, and Yi Jin. "Graph-based visual odometry for VSLAM." Industrial Robot: An International Journal 45, no. 5 (August 20, 2018): 679–87. http://dx.doi.org/10.1108/ir-04-2018-0061.

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Purpose Typical feature-matching algorithms use only unary constraints on appearances to build correspondences where little structure information is used. Ignoring structure information makes them sensitive to various environmental perturbations. The purpose of this paper is to propose a novel graph-based method that aims to improve matching accuracy by fully exploiting the structure information. Design/methodology/approach Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner. Findings The authors compare it with several state-of-the-art visual simultaneous localization and mapping algorithms on three datasets. Experimental results reveal that the ORB-G algorithm provides more accurate and robust trajectories in general. Originality/value Instead of viewing a frame as a simple collection of keypoints, the proposed approach organizes a frame as a graph by treating each keypoint as a vertex, where structure information is integrated in edges between vertices. Subsequently, the matching process of finding keypoint correspondence is formulated in a graph matching manner.
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OKADA, Nobuya, Daichi ABE, Satoshi SUZUKI, Kojiro IIZUKA, and Takashi KAWAMURA. "2A2-R04 Image and Shape features combined Landmarks based Graph SLAM(Localization and Mapping)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2014 (2014): _2A2—R04_1—_2A2—R04_4. http://dx.doi.org/10.1299/jsmermd.2014._2a2-r04_1.

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Dissertations / Theses on the topic "Graph-based localization and mapping"

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Agarwal, Pratik [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Robust graph-based localization and mapping = Robuste Graph-basierte Lokalisierung und Kartierung." Freiburg : Universität, 2015. http://d-nb.info/111980549X/34.

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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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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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Jama, Michal. "Monocular vision based localization and mapping." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/8561.

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Doctor of Philosophy
Department of Electrical and Computer Engineering
Balasubramaniam Natarajan
Dale E. Schinstock
In this dissertation, two applications related to vision-based localization and mapping are considered: (1) improving navigation system based satellite location estimates by using on-board camera images, and (2) deriving position information from video stream and using it to aid an auto-pilot of an unmanned aerial vehicle (UAV). In the first part of this dissertation, a method for analyzing a minimization process called bundle adjustment (BA) used in stereo imagery based 3D terrain reconstruction to refine estimates of camera poses (positions and orientations) is presented. In particular, imagery obtained with pushbroom cameras is of interest. This work proposes a method to identify cases in which BA does not work as intended, i.e., the cases in which the pose estimates returned by the BA are not more accurate than estimates provided by a satellite navigation systems due to the existence of degrees of freedom (DOF) in BA. Use of inaccurate pose estimates causes warping and scaling effects in the reconstructed terrain and prevents the terrain from being used in scientific analysis. Main contributions of this part of work include: 1) formulation of a method for detecting DOF in the BA; and 2) identifying that two camera geometries commonly used to obtain stereo imagery have DOF. Also, this part presents results demonstrating that avoidance of the DOF can give significant accuracy gains in aerial imagery. The second part of this dissertation proposes a vision based system for UAV navigation. This is a monocular vision based simultaneous localization and mapping (SLAM) system, which measures the position and orientation of the camera and builds a map of the environment using a video-stream from a single camera. This is different from common SLAM solutions that use sensors that measure depth, like LIDAR, stereoscopic cameras or depth cameras. The SLAM solution was built by significantly modifying and extending a recent open-source SLAM solution that is fundamentally different from a traditional approach to solving SLAM problem. The modifications made are those needed to provide the position measurements necessary for the navigation solution on a UAV while simultaneously building the map, all while maintaining control of the UAV. The main contributions of this part include: 1) extension of the map building algorithm to enable it to be used realistically while controlling a UAV and simultaneously building the map; 2) improved performance of the SLAM algorithm for lower camera frame rates; and 3) the first known demonstration of a monocular SLAM algorithm successfully controlling a UAV while simultaneously building the map. This work demonstrates that a fully autonomous UAV that uses monocular vision for navigation is feasible, and can be effective in Global Positioning System denied environments.
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Cummins, Mark. "Probabilistic localization and mapping in appearance space." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:a34370f2-a2a9-40b5-9a2d-1c8c616ff07a.

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This thesis is concerned with the problem of place recognition for mobile robots. How can a robot determine its location from an image or sequence of images, without any prior knowledge of its position, even in a world where many places look identical? We outline a new probabilistic approach to the problem, which we call Fast Appearance Based Mapping or FAB-MAP. Our map of the environment consists of a set of discrete locations, each with an associated appearance model. For every observation collected by the robot, we compute a probability distribution over the map, and either create a new location or update our belief about the appearance of an existing location. The technique can be seen as a new type of SLAM algorithm, where the appearance of locations (rather than their position) is subject to estimation. Unlike existing SLAM systems, our appearance based technique does not rely on keeping track of the robot in any metric coordinate system. Thus it is applicable even when informative observations are available only intermittently. Solutions to the loop closure detection problem, the kidnapped robot problem and the multi-session mapping problem arise as special cases of our general approach. Abstract Our probabilistic model introduces several technical advances. The model incorporates correlations between visual features in a novel way, which is shown to improve system performance. Additionally, we explicitly compute an approximation to the partition function in our Bayesian formulation, which provides a natural probabilistic measure of when a new observation should be assigned to a location not already present in the map. The technique is applicable even in visually repetitive environments where many places look the same. Abstract Finally, we define two distinct approximate inference procedures for the model. The first of these is based on concentration inequalities and has general applicability beyond the problem considered in this thesis. The second approach, built on inverted index techniques, is tailored to our specific problem of place recognition, but achieves extreme efficiency, allowing us to apply FAB-MAP to navigation problems on the largest scale. The thesis concludes with a visual SLAM experiment on a trajectory 1,000 km long. The system successfully detects loop closures with close to 100% precision and requires average inference time of only 25 ms by the end of the trajectory.
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Lim, Yu-Xi. "Efficient wireless location estimation through simultaneous localization and mapping." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28219.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Owen, Henry; Committee Member: Copeland, John; Committee Member: Giffin, Jonathon; Committee Member: Howard, Ayanna; Committee Member: Riley, George.
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Schaefer, Alexander [Verfasser], and Wolfram [Akademischer Betreuer] Burgard. "Highly accurate lidar-based mapping and localization for mobile robots." Freiburg : Universität, 2020. http://d-nb.info/1207756016/34.

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Oliveira, Douglas Coelho Braga de. "Dynamic-object-aware simultaneous localization and mapping for augmented reality applications." Universidade Federal de Juiz de Fora (UFJF), 2018. https://repositorio.ufjf.br/jspui/handle/ufjf/8059.

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CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Realidade Aumentada (RA) é uma tecnologia que permite combinar objetos virtuais tridimensionais com um ambiente predominantemente real, de forma a construir um novo ambiente onde os objetos reais e virtuais podem interagir uns com os outros em tempo real. Para fazer isso, é necessário encontrar a pose do observador (câmera, HMD, óculos inteligentes, etc.) em relação a um sistema de coordenadas global. Geralmente, algum objeto físico conhecido é usado para marcar o referencial para as projeções e para a posição do observador. O problema de Localização e Mapeamento Simultâneo (SLAM) se origina da comunidade de robótica como uma condição necessária para se construir robôs verdadeiramente autônomos, capazes de se auto localizarem em um ambiente desconhecido ao mesmo tempo que constroem um mapa da cena observada a partir de informações capturadas por um conjunto de sensores. A principal contribuição do SLAM para a RA é permitir aplicações em ambientes despreparados, ou seja, sem marcadores. No entanto, ao eliminar o marcador, perdemos o referencial para a projeção dos objetos virtuais e a principal fonte de interação entre os elementos reais e virtuais. Embora o mapa gerado possa ser processado a fim de encontrar uma estrutura conhecida, como um plano predominante, para usá-la como referencial, isso ainda não resolve a questão das interações. Na literatura recente, encontramos trabalhos que integram um sistema de reconhecimento de objetos ao SLAM e incorporam tais objetos ao mapa. Frequentemente, assume-se um mapa estático, devido às limitações das técnicas envolvidas, de modo que o objeto é usado apenas para fornecer informações semânticas sobre a cena. Neste trabalho, propomos um novo framework que permite estimar simultaneamente a posição da câmera e de objetos para cada quadro de vídeo em tempo real. Dessa forma, cada objeto é independente e pode se mover pelo mapa livremente, assim como nos métodos baseados em marcadores, mas mantendo as vantagens que o SLAM fornece. Implementamos a estrutura proposta sobre um sistema SLAM de última geração a fim de validar nossa proposta e demonstrar a potencial aplicação em Realidade Aumentada.
Augmented Reality (AR) is a technology that allows combining three-dimensional virtual objects with an environment predominantly real in a way to build a new environment where both real and virtual objects can interact with each other in real-time. To do this, it is required to nd the pose of the observer (camera, HMD, smart glasses etc) in relation to a global coordinate system. Commonly, some well known physical object, called marker, is used to de ne the referential for both virtual objects and the observer's position. The Simultaneous Localization and Mapping (SLAM) problem borns from robotics community as a way to build truly autonomous robots by allowing they to localize themselves while they build a map of the observed scene from the input data of their coupled sensors. SLAM-based Augmented Reality is an active and evolving research line. The main contribution of the SLAM to the AR is to allow applications on unprepared environments, i.e., without markers. However, by eliminating the marker object, we lose the referential for virtual object projection and the main source of interaction between real and virtual elements. Although the generated map can be processed in order to nd a known structure, e.g. a predominant plane, to use it as the referential system, this still not solve for interactions. In the recent literature, we can found works that integrate an object recognition system to the SLAM in a way the objects are incorporated into the map. The SLAM map is frequently assumed to be static, due to limitations on techniques involved, so that on these works the object is just used to provide semantic information about the scene. In this work, we propose a new framework that allows estimating simultaneously the camera and object positioning for each camera image in real time. In this way, each object is independent and can move through the map as well as in the marker-based methods but with the SLAM advantages kept. We develop our proposed framework over a stateof- the-art SLAM system in order to evaluate our proposal and demonstrate potentials application in Augmented Reality.
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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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Droeschel, David Marcel [Verfasser]. "Efficient Methods for Lidar-based Mapping and Localization / David Marcel Droeschel." Bonn : Universitäts- und Landesbibliothek Bonn, 2020. http://d-nb.info/122166929X/34.

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Books on the topic "Graph-based localization and mapping"

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Dyer, Paul S., Carol A. Munro, and Rosie E. Bradshaw. Fungal genetics. Edited by Christopher C. Kibbler, Richard Barton, Neil A. R. Gow, Susan Howell, Donna M. MacCallum, and Rohini J. Manuel. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198755388.003.0005.

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Fungi have been long used as model organisms to investigate genetic and cellular processes. An overview is provided of how fungi function at a genetic level, including ploidy, gene structure, and gene flow by sexual and asexual processes. The tools used to study fungal genetics are then described, such techniques having widespread applications in medical mycology research. Classical genetic analysis includes the use of gene mapping by sexual crossing and tetrad analysis, and forward genetic experimentation based on mutagenesis, for which various mutant screening approaches are described. Molecular genetic analysis includes gene manipulation by transformation; different methods for gene knockout and targeting, and their application for forward and reverse genetic approaches, are outlined. Finally, molecular genetic methods used to study gene expression and function are reviewed, including use of inducible or constitutive overexpression, real-time PCR, cellular localization of gene products by fluorescent tagging, and detection of protein–protein interactions.
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Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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Chung, Moo K. Statistical and Computational Methods in Brain Image Analysis. Taylor & Francis Group, 2013.

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Practical R for biologists: an introduction. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0000.

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Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.
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Book chapters on the topic "Graph-based localization and mapping"

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Werner, Martin. "Simultaneous Localization and Mapping in Buildings." In Indoor Location-Based Services, 181–201. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10699-1_8.

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Wallgrün, Jan Oliver. "Voronoi Graph Matching for Robot Localization and Mapping." In Transactions on Computational Science IX, 76–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16007-3_4.

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Zhu, Xiaorui, Youngshik Kim, Mark Andrew Minor, and Chunxin Qiu. "Terrain-Inclination–Based Localization and Mapping." In Autonomous Mobile Robots in Unknown Outdoor Environments, 187–204. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2017. |: CRC Press, 2017. http://dx.doi.org/10.1201/9781315151496-9.

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Zhou, Mu, Qiao Zhang, Zengshan Tian, Feng Qiu, and Qing Jiang. "WLAN Localization Without Location Fingerprinting Using Logic Graph Mapping." In Lecture Notes in Electrical Engineering, 545–56. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_56.

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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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Tsintotas, Konstantinos A., Loukas Bampis, and Antonios Gasteratos. "The Revisiting Problem in Simultaneous Localization and Mapping." In Online Appearance-Based Place Recognition and Mapping, 1–33. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09396-8_1.

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Liu, Jiayi, Randy C. Hoover, and Jeff S. McGough. "Mobile Fiducial-Based Collaborative Localization and Mapping (CLAM)." In Proceedings of the 2020 USCToMM Symposium on Mechanical Systems and Robotics, 196–205. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43929-3_18.

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Garcia-Fidalgo, Emilio, and Alberto Ortiz. "Probabilistic Appearance-Based Mapping and Localization Using Visual Features." In Pattern Recognition and Image Analysis, 277–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_33.

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Weikersdorfer, David, Raoul Hoffmann, and Jörg Conradt. "Simultaneous Localization and Mapping for Event-Based Vision Systems." In Lecture Notes in Computer Science, 133–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39402-7_14.

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Bryson, Mitch, and Salah Sukkarieh. "Inertial Sensor-Based Simultaneous Localization and Mapping for UAVs." In Handbook of Unmanned Aerial Vehicles, 401–31. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-90-481-9707-1_5.

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Conference papers on the topic "Graph-based localization and mapping"

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Carlone, Luca, Rosario Aragues, Jose Castellanos, and Basilio Bona. "A Linear Approximation for Graph-based Simultaneous Localization and Mapping." In Robotics: Science and Systems 2011. Robotics: Science and Systems Foundation, 2011. http://dx.doi.org/10.15607/rss.2011.vii.006.

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Leitinger, Erik, Florian Meyer, Fredrik Tufvesson, and Klaus Witrisal. "Factor graph based simultaneous localization and mapping using multipath channel information." In 2017 IEEE International Conference on Communications Workshops (ICC Workshops). IEEE, 2017. http://dx.doi.org/10.1109/iccw.2017.7962732.

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Yin, Jingchun, Luca Carlone, Stefano Rosa, and Basilio Bona. "Graph-based robust localization and mapping for autonomous mobile robotic navigation." In 2014 IEEE International Conference on Mechatronics and Automation (ICMA). IEEE, 2014. http://dx.doi.org/10.1109/icma.2014.6885953.

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Beinschob, Patric, and Christoph Reinke. "Graph SLAM based mapping for AGV localization in large-scale warehouses." In 2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2015. http://dx.doi.org/10.1109/iccp.2015.7312637.

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Maddern, Will, Michael Milford, and Gordon Wyeth. "Towards persistent indoor appearance-based localization, mapping and navigation using CAT-Graph." In 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012). IEEE, 2012. http://dx.doi.org/10.1109/iros.2012.6386186.

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Zhou, Mu, Qiao Zhang, Zengshan Tian, Kunjie Xu, Feng Qiu, and Qi Wu. "Graph drawing based WLAN indoor mapping and localization using signal correlation via edge detection." In 2015 IEEE International Wireless Symposium (IWS). IEEE, 2015. http://dx.doi.org/10.1109/ieee-iws.2015.7164524.

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Babu, Benzun P. Wisely, David Cyganski, and James Duckworth. "Gyroscope assisted scalable visual simultaneous localization and mapping." In 2014 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS). IEEE, 2014. http://dx.doi.org/10.1109/upinlbs.2014.7033731.

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Zheng, Junyuan, Yuan He, and Masaaki Kondo. "Exploiting Data Parallelism in Graph-Based Simultaneous Localization and Mapping: A Case Study with GPU Accelerations." In HPC ASIA 2023: International Conference on High Performance Computing in Asia-Pacific Region. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3578178.3578237.

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Danping, Jia, Duan Guangxue, Wang Nan, Zhou Zhigang, Zhong Zhenyu, and Lei Huan. "Simultaneous Localization and Mapping based on Lidar." In 2019 Chinese Control And Decision Conference (CCDC). IEEE, 2019. http://dx.doi.org/10.1109/ccdc.2019.8833308.

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Meghdari, A., K. Kobravi, H. Safyallah, M. Moeeni, Y. Khatami, and H. Khasteh. "A New Approach to Sonar Based Indoor Mapping Localization." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85269.

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Vehicle localization and environment mapping are the most essential parts of the robot navigation in unknown environments. Since the problem of localization in indoor environments is directly related to the problem of online map generation, in this paper a new and efficient algorithm for simultaneous localization and map generation is proposed and novel results for real environments are achieved. This new algorithm interprets and validates the raw sonar measurements in first step, and applies them to the environment map in the next step. There are various adjustable parameters which make the algorithm flexible for different sonar types. This algorithm is efficient and is robust to sonar failure; if sonar does not work properly data can be discarded. These abilities make the algorithm efficient for sonar navigation in flat environments even by poor sonar and odometers perception data. This algorithm has the ability of matching with various types of sonar and even to be used with laser scanner data, whenever each laser scanner data is treated as multiple sonar detections with narrow beam detection patterns.
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Reports on the topic "Graph-based localization and mapping"

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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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Lee, W. S., Victor Alchanatis, and Asher Levi. Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management. United States Department of Agriculture, January 2014. http://dx.doi.org/10.32747/2014.7598158.bard.

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Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved.
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Wan, Wei. A New Approach to the Decomposition of Incompletely Specified Functions Based on Graph Coloring and Local Transformation and Its Application to FPGA Mapping. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6582.

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Bennett, Alan B., Arthur A. Schaffer, Ilan Levin, Marina Petreikov, and Adi Doron-Faigenboim. Manipulating fruit chloroplasts as a strategy to improve fruit quality. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598148.bard.

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The Original Objectives were modified and two were eliminated to reflect the experimental results: Objective 1 - Identify additional genetic variability in SlGLK2 and IPin wild, traditional and heirloom tomato varieties Objective 2 - Determine carbon balance and horticultural characteristics of isogenic lines expressing functional and non-functional alleles of GLKsand IP Background: The goal of the research was to understand the unique aspects of chloroplasts and photosynthesis in green fruit and the consequences of increasing the chloroplast capacity of green fruit for ripe fruit sugars, yield, flavor and nutrient qualities. By focusing on the regulation of chloroplast formation and development solely in fruit, our integrated knowledge of photosynthetic structures/organs could be broadened and the results of the work could impact the design of manipulations to optimize quality outputs for the agricultural fruit with enhanced sugars, nutrients and flavors. The project was based on the hypothesis that photosynthetic and non-photosynthetic plastid metabolism in green tomato fruit is controlled at a basal level by light for minimal energy requirements but fruit-specific genes regulate further development of robust chloroplasts in this organ. Our BARD project goals were to characterize and quantitate the photosynthesis and chloroplast derived products impacted by expression of a tomato Golden 2- like 2 transcription factor (US activities) in a diverse set of 31 heirloom tomato lines and examine the role of another potential regulator, the product of the Intense Pigment gene (IP activities). Using tomato Golden 2-like 2 and Intense Pigment, which was an undefined locus that leads to enhanced chloroplast development in green fruit, we sought to determine the benefits and costs of extensive chloroplast development in fruit prior to ripening. Major conclusions, solutions, achievements: Single nucleotide polymorphisms in the promoter, coding and intronicSlGLK2 sequences of 20 heirloom tomato lines were identified and three SlGLK2 promoter lineages were identified; two lineages also had striped fruit variants. Lines with striped fruit but no shoulders were not identified. Green fruit chlorophyll and ripe fruit soluble sugar levels were measured in 31 heirloom varieties and fruit size correlates with ripe fruit sugars but dark shoulders does not. A combination of fine mapping, recombinant generation, RNAseq expression and SNP calling all indicated that the proposed localization of a single locus IP on chr 10 was incorrect. Rather, the IP line harbored 11 separate introgressions from the S. chmielewskiparent, scattered throughout the genome. These introgressions harbored ~3% of the wild species genome and no recombinant consistently recovered the IP parental phenotype. The 11 introgressions were dissected into small combinations in segregating recombinant populations. Based on these analyses two QTL for Brix content were identified, accounting for the effect of increased Brix in the IP line. Scientific and agricultural implications: SlGLK2 sequence variation in heirloom tomato varieties has been identified and can be used to breed for differences in SlGLK2 expression and possibly in the green striped fruit phenotype. Two QTL for Brix content have been identified in the S. chmielewskiparental line and these can be used for increasing soluble solids contents in breeding programs.
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