Relevant bibliographies by topics / Mobile robot mapping

Academic literature on the topic 'Mobile robot mapping'

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Published: 14 March 2023

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Journal articles on the topic "Mobile robot mapping"

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

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Simultaneous localization and mapping (SLAM) forms the core of the technology that supports mobile robots. With SLAM, when a robot is moving in an actual environment, real world information is imported to a computer on the robot via a sensor, and robot’s physical location and a map of its surrounding environment of the robot are created. SLAM is a major topic in mobile robot research. Although the information, supported by a mathematical description, is derived from a space in reality, it is formulated based on a probability theory when being handled. Therefore, this concept contributes not only to the research and development concerning mobile robots, but also to the training of mathematics and computer implementation, aimed mainly at position estimation and map creation for the mobile robots. This article focuses on the SLAM technology, including a brief overview of its history, insights from the author, and, finally, introduction of a specific example that the author was involved.
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Ridlwan, Hasvienda Mohammad, Sonki Prasetya, and Musli Min. "2D Mapping Lingkungan Indoor Menggunakan Lidar dan ROS untuk Mobile Robot." Jurnal Mekanik Terapan 3, no. 2 (August 31, 2022): 60–65. http://dx.doi.org/10.32722/jmt.v3i2.4285.

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Currently, the application of control systems has been applied in various scientific fields including mechatronics and robotics. Applications in the branch of robotics are also growing day by day not only with conventional controls but also with intelligent systems. An autonomous robot in carrying out certain missions in an unknown environment requires information about the location itself and the environment through the map. A process to identify a position without a map is called a localization function on the robot. Mobile robots building maps and localization are two fundamental tasks when mobile robots work in indoor environments. With 2D laser scanning (LiDAR) data obtained in real-time, the robot can calculate the area of ​​all empty spaces in a room, then can choose the center of the room as its position for map building. The objective of this research is to implement a two-dimensional mapping method using LiDAR. The algorithm used in this study is the Gmapping Technique on ROS. The main purpose of this research is to map mobile robots with LIDAR sensors using the Robot Operating System for navigation and positioning of mobile robots. Through the actual experimental results, the mobile robot will move with a 2-dimensional mapping process.
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Szeląg, Piotr, Sebastian Dudzik, and Anna Podsiedlik. "Investigation on the Mobile Wheeled Robot in Terms of Energy Consumption, Travelling Time and Path Matching Accuracy." Energies 16, no. 3 (January 22, 2023): 1210. http://dx.doi.org/10.3390/en16031210.

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The task of controlling a wheeled mobile robot is an important element of navigation algorithms. The control algorithm manages the robot’s movement in accordance with the path determined by the planner module, where the accuracy of mapping the given route is very important. Most often, mobile robots are battery-powered, which makes minimizing energy consumption and shortening travel time an important issue. For this reason, in this work, the mobile robot control algorithm was tested in terms of energy consumption, travel time and path mapping accuracy. During the research, a criterion was developed, thanks to which it was possible to select the optimal parameters of the pure pursuit algorithm that controls the movement of the tested robot. The research was carried out in the Laboratory of Intelligent Mobile Robots using the QBot2e mobile robot operating on the basis of differential drive kinematics. As a result of the research, optimal values of the control parameters were obtained, minimizing the travel time, energy consumption and mapping error of the given paths.
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Umetani, Tomohiro, Yuya Kondo, and Takuma Tokuda. "Rapid Development of a Mobile Robot for the Nakanoshima Challenge Using a Robot for Intelligent Environments." Journal of Robotics and Mechatronics 32, no. 6 (December 20, 2020): 1211–18. http://dx.doi.org/10.20965/jrm.2020.p1211.

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Automated mobile platforms are commonly used to provide services for people in an intelligent environment. Data on the physical position of personal electronic devices or mobile robots are important for information services and robotic applications. Therefore, automated mobile robots are required to reconstruct location data in surveillance tasks. This paper describes the development of an autonomous mobile robot to achieve tasks in intelligent environments. In particular, the robot constructed route maps in outdoor environments using laser imaging detection and ranging (LiDAR), and RGB-D sensors via simultaneous localization and mapping. The mobile robot system was developed based on a robot operating system (ROS), reusing existing software. The robot participated in the Nakanoshima Challenge, which is an experimental demonstration test of mobile robots in Osaka, Japan. The results of the experiments and outdoor field tests demonstrate the feasibility of the proposed robot system.
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Deo, Ankit, Ayush Gupta, Himanshu Khemani, and Rashmi Ranjan Das. "Path tracking mobile robot using steppers." E3S Web of Conferences 87 (2019): 01028. http://dx.doi.org/10.1051/e3sconf/20198701028.

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In control of mobile robots, precision plays a key role in path tracking. In this paper we have intended to use hybrid stepper motors for precise control of the two wheeled robot. A control algorithm was developed to control the robot along different trajectories. We have found that stepper motors are more accurate for path tracking than normal DC motors with wheel encoders and one can obtain the implicit coordinates of the robot in runtime more precisely. Getting the precise coordinates of the robot at runtime can be used in various SLAM and VSLAM techniques for more accurate 3D mapping of the environment.
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Sujan, Vivek Anand, Marco Antonio Meggiolaro, and Felipe Augusto Weilemann Belo. "A new technique in mobile robot simultaneous localization and mapping." Sba: Controle & Automação Sociedade Brasileira de Automatica 17, no. 2 (June 2006): 189–204. http://dx.doi.org/10.1590/s0103-17592006000200007.

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In field or indoor environments it is usually not possible to provide service robots with detailed a priori environment and task models. In such environments, robots will need to create a dimensionally accurate geometric model by moving around and scanning the surroundings with their sensors, while minimizing the complexity of the required sensing hardware. In this work, an iterative algorithm is proposed to plan the visual exploration strategy of service robots, enabling them to efficiently build a graph model of their environment without the need of costly sensors. In this algorithm, the information content present in sub-regions of a 2-D panoramic image of the environment is determined from the robot's current location using a single camera fixed on the mobile robot. Using a metric based on Shannon's information theory, the algorithm determines, from the 2-D image, potential locations of nodes from which to further image the environment. Using a feature tracking process, the algorithm helps navigate the robot to each new node, where the imaging process is repeated. A Mellin transform and tracking process is used to guide the robot back to a previous node. This imaging, evaluation, branching and retracing its steps continues until the robot has mapped the environment to a pre-specified level of detail. The effectiveness of this algorithm is verified experimentally through the exploration of an indoor environment by a single mobile robot agent using a limited sensor suite.
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Ganiev, Asilbek, and Kang Hee Lee. "A Study of Autonomous Navigation of a Robot Model based on SLAM, ROS, and Kinect." International Journal of Engineering & Technology 7, no. 3.33 (August 29, 2018): 28. http://dx.doi.org/10.14419/ijet.v7i3.33.18517.

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In this paper, we used a robot operating system (ROS) that is designed to work with mobile robots. ROS provides us with simultaneous localization and mapping of the environment, and here it is used to autonomously navigate a mobile robot simulator between specified points. Also, when the mobile robot automatically navigates between the starting point and the target point, it bypasses obstacles; and if necessary, sets a new path of the route to reach the goal point.
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Nagla, KS, Moin Uddin, and Dilbag Singh. "Dedicated Filter for Robust Occupancy Grid Mapping." IAES International Journal of Robotics and Automation (IJRA) 4, no. 1 (March 1, 2014): 82. http://dx.doi.org/10.11591/ijra.v4i1.pp82-92.

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<p>Sensor based perception of the environment is an emerging area of the mobile robot research where sensors play a pivotal role. For autonomous mobile robots, the fundamental requirement is the convergent of the range information in to high level internal representation. Internal representation in the form of occupancy grid is commonly used in autonomous mobile robots due to its various advantages. There are several sensors such as vision sensor, laser rage finder, and ultrasonic and infrared sensors etc. play roles in mapping. However the sensor information failure, sensor inaccuracies, noise, and slow response are the major causes of an error in the mapping. To improve the reliability of the mobile robot mapping multisensory data fusion is considered as an optimal solution. This paper presents a novel architecture of sensor fusion frame work in which a dedicated filter (DF) is proposed to increase the robustness of the occupancy grid for indoor environment. The technique has been experimentally verified for different indoor test environments. The proposed configuration shows improvement in the occupancy grid with the implementation of dedicated filters.</p>
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Achour, Abdessalem, Hiba Al-Assaad, Yohan Dupuis, and Madeleine El Zaher. "Collaborative Mobile Robotics for Semantic Mapping: A Survey." Applied Sciences 12, no. 20 (October 13, 2022): 10316. http://dx.doi.org/10.3390/app122010316.

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Ensuring safety in human–robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted.
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Qin Zhang, and Qingxuan Jia. "Indoor Semantic Mapping for Mobile Robot." International Journal of Digital Content Technology and its Applications 6, no. 22 (December 31, 2012): 648–57. http://dx.doi.org/10.4156/jdcta.vol6.issue22.74.

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Dissertations / Theses on the topic "Mobile robot mapping"

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HASSANZADEH, Aidin. "Mobile Robot Wind Mapping." Thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-34606.

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Statistical gas distribution mapping has recently become a prominent research area in the robotics community. Gas distribution mapping using mobile robots aims for building map of gas dispersion in an unknown environment using the sampled gas concentrations accompanied by the corresponding atmospheric variables. In this context, wind is considered as one of the main driving forces and recently exploited as an environmental bias in the the modelling process. However, the existing approaches utilizing the wind data are based on very simple averaging window methods which do not take the specic spatio-temporal wind variations into account appropriately. In the current thesis work, under the heading of statistical wind modelling, the various aspects of the existing approaches to model both temporal and spatial wind variations are studied. Accordingly, in the undertaking of Mobile Robot Wind Mapping (MRWM) task, three individual methods for statistically wind speed modelling, wind direction modelling and spatial wind mapping are proposed and implemented. Particularly, wind speed is modelled in form of a Gaussian distribution where the valid averaging scale is dened using an online adaptive approach, namely Time-Dependent Memory Method (TDMM) . The wind direction is modelled by means of the mixturemodel of Von-Mises distribution and for the spatial mapping of modelled wind data, a recursive approach based on Linear Kalman lter is utilized. The proposed approaches for statistically wind speed and direction modelling are applied to and evaluated by real wind data, collected specically for this project. The wind mapping algorithm is implemented and tested using simulated data.
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Wong, Chee Kit. "Cognitive inspired mapping by an autonomous mobile robot." Click here to access this resource online, 2008. http://hdl.handle.net/10292/427.

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When animals explore a new environment, they do not acquire a precise map of the places visited. In fact, research has shown that learning is a recurring process. Over time, new information helps the animal to update their perception of the locations it has visited. Yet, they are still able to use the fuzzy and often incomplete representation to find their way home. This process has been termed the cognitive mapping process. The work presented in this thesis uses a mobile robot equipped with sonar sensors to investigate the nature of such a process. Specifically, what is the information that is fundamental and prevalent in spatial navigation? Initially, the robot is instructed to compute a “cognitive map” of its environment. Since a robot is not a cognitive agent, it cannot, by definition, compute a cognitive map. Hence the robot is used as a test bed for understanding the cognitive mapping process. Yeap’s (1988) theory of cognitive mapping forms the foundation for computing the robot’s representation of the places it has visited. He argued that a network of local spaces is computed early in the cognitive mapping process. Yeap coined these local spaces as Absolute Space Representations (ASRs). However, ASR is not just a process of partitioning the environment into smaller local regions. The ASRs describe the bounded space that one is in, how one could leave that space (exits) and how the exits serves to link the ASRs to form a network that serves as the cognitive map (see Jefferies (1999)). Like the animal’s cognitive map, ASRs are not precise geometrical maps of the environment but rather, provide a rough shape or feel of the space the robot is currently in. Once the robot computes its “cognitive map”, it is then, like foraging and hoarding animals, instructed to find its way home. To do so, the robot uses two crucial pieces of information: distance between exits of ASRs and relative orientation of adjacent ASRs. A simple animal-like strategy was implemented for the robot to locate home. Results from the experiments demonstrated the robot’s ability to determine its location within the visited environment along its journey. This task was performed without the use of an accurate map. From these results and reviews of various findings related to cognitive mapping for various animals, we deduce that: Different animals have different sensing capabilities. They live in different environments and therefore face unique challenges. Consequently, they evolve to have different navigational strategies. However, we believe two crucial pieces of information are inherent in all animals and form the fundamentals of navigation: distance and orientation. Higher level animals may encode and may even prefer richer information to enhance the animal’s cognitive map. Nonetheless, distance and orientation will always be computed as a core process of cognitive mapping. We believe this insight will help future research to better understand the complex nature of cognitive mapping.
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WANG, XUAN. "2D Mapping Solutionsfor Low Cost Mobile Robot." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-138427.

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Mapping, localization, and path-planning are three fundamental problems of robotic. Robot needs a map to perform actions like path-planning. When positioning system is not available, the map is also used for localization. A lot of researches have been done in this area. And newly emerging ranging sensors, like Kinect and TOF camera, have widen people’s choices and greatly enhanced innovative ideas in robot mapping. The price of these sensors is not very high and the performance is decent, which makes low cost, high performance mobile robot solution possible. In this thesis, different existing state of the art mapping methods are studied. Based on literature studies, different ranging sensors for mapping are evaluated. And by using the 3D ranging sensor, three mapping methods are implemented. Occupancy grid map with scan matching, feature-grid hybrid map with map pruning and simple points map with ICP algorithm. Basic potential field path-planning is also implemented. The experiment results illustrate the performance of each method.
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Nordin, Peter. "Mobile Robot Traversability Mapping : For Outdoor Navigation." Licentiate thesis, Linköpings universitet, Fluida och mekatroniska system, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-85937.

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To avoid getting stuck or causing damage to a vehicle or its surroundings a driver must be able to identify obstacles and adapt speed to ground conditions. An automatically controlled vehicle must be able to handle these identifications and adjustments by itself using sensors, actuators and control software. By storing properties of the surroundings in a map, a vehicle revisiting an area can benefit from prior information. Rough ground may cause oscillations in the vehicle chassis. These can be measured by on-board motion sensors. For obstacle detection, a representation of the geometry of the surroundings can be created using range sensors. Information on where it is suitable to drive, called traversability, can be generated based on these kinds of sensor measurements. In this work, real semi-autonomous mobile robots have been used to create traverasbility maps in both simulated and real outdoor environments. Seeking out problems through experiments and implementing algorithms in an attempt to solve them has been the core of the work. Finding large obstacles in the vicinity of a vehicle is seldom a problem; accurately identifying small near-ground obstacles is much more difficult, however. The work additionally includes both high-level path planning, where no obstacle details are considered, and more detailed planning for finding an obstacle free path. How prior maps can be matched and merged in preparation for path planning operations is also shown. To prevent collisions with unforeseen objects, up-to-date traversability information is used in local-area navigation and obstacle avoidance.
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Deďo, Michal. "Řízení čtyřkolového mobilního robotu." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229688.

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The purpose of this thesis is to design and implement four-wheel mobile robot control which will be used in future in the field of mapping and localization. Concretely, it will be a design of drive control with microcontrollers Xmega, which will also process the signals of the sensors. Communication with the PC will ensure the BlueTooth module. In view of the future use of the robot, there will be designed and carried out modifications of the mechanical part. Correctness and functionality of all parts of the robot will be verified by carrying out basic movements.
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McCoig, Kenneth. "A MOBILE ROBOTIC COMPUTING PLATFORM FOR THREE-DIMENSIONAL INDOOR MAPPI." Master's thesis, University of Central Florida, 2004. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2372.

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There are several industries exploring solutions to quickly and accurately digitize unexplored indoor environments, into useable three-dimensional databases. Unfortunately, there are inherent challenges to the indoor mapping process such as, scanning limitations and environment complexity, which require a specific application of tools to map an environment precisely with low cost and high speed. This thesis successfully demonstrates the design and implementation of a low cost mobile robotic computing platform with laser scanner, for quickly mapping with high resolution, urban and/or indoor environments using a gyro-enhanced orientation sensor and selectable levels of detail. In addition, a low cost alternative solution to three-dimensional laser scanning is presented, via a standard two-dimensional SICK proximity laser scanner mounted to a custom servo motor mount and controlled by external microcontroller. A software system to control the robot is presented, which incorporates and adheres to widely accepted software engineering guidelines and principles. An analysis of the overall system, including robot specifications, system capabilities, and justification for certain design decisions, are described in detail. Results of various open source software algorithms, as it applies to scan data and image data, are also compared; including evaluation of data correlation and registration techniques. In addition, laser scanner mapping tests, specifications, and capabilities are presented and analyzed. A sample design for converting the final scanned point cloud data to a database is presented and assessed. The results suggest the overall project yields a relatively high degree of accuracy and lower cost over most other existing systems surveyed, as well as, the potential for application of the system in other fields. The results also discuss thoughts for possible future research work.
M.S.Cp.E.
Department of Electrical and Computer Engineering
Engineering and Computer Science
Computer Engineering
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Ezequiel, Carlos Favis. "Real-Time Map Manipulation for Mobile Robot Navigation." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4481.

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Mobile robots are gaining increased autonomy due to advances in sensor and computing technology. In their current form however, robots still lack algorithms for rapid perception of objects in a cluttered environment and can benefit from the assistance of a human operator. Further, fully autonomous systems will continue to be computationally expensive and costly for quite some time. Humans can visually assess objects and determine whether a certain path is traversable, but need not be involved in the low-level steering around any detected obstacles as is necessary in remote-controlled systems. If only used for rapid perception tasks, the operator could potentially assist several mobile robots performing various tasks such as exploration, surveillance, industrial work and search and rescue operations. There is a need to develop better human-robot interaction paradigms that would allow the human operator to effectively control and manage one or more mobile robots. This paper proposes a method of enhancing user effectiveness in controlling multiple mobile robots through real-time map manipulation. An interface is created that would allow a human operator to add virtual obstacles to the map that represents areas that the robot should avoid. A video camera is connected to the robot that would allow a human user to view the robot's environment. The combination of real-time map editing and live video streaming enables the robot to take advantage of human vision, which is still more effective at general object identification than current computer vision technology. Experimental results show that the robot is able to plan a faster path around an obstacle when the user marks the obstacle on the map, as opposed to allowing the robot to navigate on its own around an unmapped obstacle. Tests conducted on multiple users suggest that the accuracy in placing obstacles on the map decreases with increasing distance of the viewing apparatus from the obstacle. Despite this, the user can take advantage of landmarks found in the video and in the map in order to determine an obstacle's position on the map.
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Casalaro, Giuseppina Lucia, and Giulio Cattivera. "MODEL-DRIVEN ENGINEERING FOR MOBILE ROBOT SYSTEMS: A SYSTEMATIC MAPPING STUDY." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-28261.

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The development of autonomous Mobile Robot Systems is attracting nowadays more and moreinterest from both researchers and practitioners, mainly because they may open for a wide rangeof improvements for quality of life. Mobile robots are systems capable of accomplishing missionsby moving in an unknown environment without human supervision. Throughout mechanisms ofdetection, communication and adaptation, they can adapt their behavior according to changes ofthe environment. Individual robots can even join teams of autonomous mobile robots that, throughindividual tasks, accomplish common missions. These are called Mobile Multi-Robot Systems andare meant to perform missions that a single robot would not be able to carry out by itself.When it comes to the development of Mobile Robot Systems, currently there is no standard methodology.This is mainly due to the complexity of the domain and the variety of di↵erent platformsthat are available on the market. A promising methodology that recently has gained attention insoftware industry for its ability of mitigating complexity and boosting platform-independence, isModel-Driven Engineering.This thesis proposes a systematic mapping study on the state-of-the-art of Model-Driven Engineeringfor Mobile Robot Systems. Through our contribution, researchers can get a picture of theactual trends and open challenges for further research, while practitioners can realize the suitabilityof Model-Driven Engineering by checking to what extent it has been applied to real-world projects.
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Williams, Stefan Bernard. "Efficient Solutions to Autonomous Mapping and Navigation Problems." University of Sydney. Aerospace, Mechanical and Mechatronic Engineering, 2002. http://hdl.handle.net/2123/809.

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This thesis deals with the Simultaneous Localisation and Mapping algorithm as it pertains to the deployment of mobile systems in unknown environments. Simultaneous Localisation and Mapping (SLAM) as defined in this thesis is the process of concurrently building up a map of the environment and using this map to obtain improved estimates of the location of the vehicle. In essence, the vehicle relies on its ability to extract useful navigation information from the data returned by its sensors. The vehicle typically starts at an unknown location with no a priori knowledge of landmark locations. From relative observations of landmarks, it simultaneously computes an estimate of vehicle location and an estimate of landmark locations. While continuing in motion, the vehicle builds a complete map of landmarks and uses these to provide continuous estimates of the vehicle location. The potential for this type of navigation system for autonomous systems operating in unknown environments is enormous. One significant obstacle on the road to the implementation and deployment of large scale SLAM algorithms is the computational effort required to maintain the correlation information between features in the map and between the features and the vehicle. Performing the update of the covariance matrix is of O(n�) for a straightforward implementation of the Kalman Filter. In the case of the SLAM algorithm, this complexity can be reduced to O(n�) given the sparse nature of typical observations. Even so, this implies that the computational effort will grow with the square of the number of features maintained in the map. For maps containing more than a few tens of features, this computational burden will quickly make the update intractable - especially if the observation rates are high. An effective map-management technique is therefore required in order to help manage this complexity. The major contributions of this thesis arise from the formulation of a new approach to the mapping of terrain features that provides improved computational efficiency in the SLAM algorithm. Rather than incorporating every observation directly into the global map of the environment, the Constrained Local Submap Filter (CLSF) relies on creating an independent, local submap of the features in the immediate vicinity of the vehicle. This local submap is then periodically fused into the global map of the environment. This representation is shown to reduce the computational complexity of maintaining the global map estimates as well as improving the data association process by allowing the association decisions to be deferred until an improved local picture of the environment is available. This approach also lends itself well to three natural extensions to the representation that are also outlined in the thesis. These include the prospect of deploying multi-vehicle SLAM, the Constrained Relative Submap Filter and a novel feature initialisation technique. Results of this work are presented both in simulation and using real data collected during deployment of a submersible vehicle equipped with scanning sonar.
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Sezginalp, Emre. "Simultaneous Localization And Mapping For A Mobile Robot Operating In Outdoor Environments." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609191/index.pdf.

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In this thesis, a method to the solution of autonomous navigation problem of a robot working in an outdoor application is sought. The robot will operate in unknown terrain where there is no a priori map present, and the robot must localize itself while simultaneously mapping the environment. This is known as Simultaneous Localization and Mapping (SLAM) problem in the literature. The SLAM problem is attempted to be solved by using the correlation between range data acquired at different poses of the robot. A robot operating outdoors will traverse unstructured terrain, therefore for localization, pitch, yaw and roll angles must also be taken into account along with the (x,y,z) coordinates of the robot. The Iterative Closest Points (ICP) algorithm is used to find this transformation between different poses of the robot and find its location. In order to collect the range data, a system composing of a laser range finder and an angular positioning system is used. During localization and mapping, odometry data is fused with range data.
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Books on the topic "Mobile robot mapping"

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Mullane, John. Random Finite Sets for Robot Mapping and SLAM: New Concepts in Autonomous Robotic Map Representations. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Milford, Michael John. Robot navigation from nature: Simultaneous localisation, mapping, and path planning based on hippocampal models. Berlin: Springer, 2008.

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Kucner, Tomasz Piotr, Achim J. Lilienthal, Martin Magnusson, Luigi Palmieri, and Chittaranjan Srinivas Swaminathan. Probabilistic Mapping of Spatial Motion Patterns for Mobile Robots. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41808-3.

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Robotic navigation and mapping with radar. Boston: Artech House, 2012.

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Fernández-Madrigal, Juan-Antonio. Simultaneous localization and mapping for mobile robots: Introduction and methods. Hershey, PA: Information Science Reference, 2013.

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Bayoud, Fadi Atef. Development of a robotic mobile mapping system by vision-aided inertial navigation: A geomatics approach. Zürich: Institut für Geodäsie und Photogrammetrie, Eidgenössische Technische Hochschule Zürich, 2006.

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Development of a robotic mobile mapping system by vision-aided inertial navigation: A geomatics approach. Zürich: Institut für Geodäsie und Photogrammetrie, Eidgenössische Technische Hochschule Zürich, 2006.

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Hafner, Verena Vanessa. Adaptive navigation strategies in biorobotics: Visual homing and cognitive mapping in animals and machines. Aachen: Shaker, 2004.

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Adaptive Sampling With Mobile Wsn Simultaneous Robot Localisation And Mapping Of Paramagnetic Spatiotemporal Fields. Institution of Engineering & Technology (IET), 2011.

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Lewis, Frank L., Koushil Sreenath, Muhammad F. Mysorewala, and Dan O. Popa. Adaptive Sampling with Mobile WSN: Simultaneous Robot Localisation and Mapping of Paramagnetic Spatio-Temporal Fields. Institution of Engineering & Technology, 2011.

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Book chapters on the topic "Mobile robot mapping"

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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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Krishna, K. Y. V., A. Wadnerkar, G. M. Patel, G. Baluni, A. K. Pandey, and R. M. Suresh Babu. "Indigenous Mobile Robot for Surveillance and Mapping." In Lecture Notes in Mechanical Engineering, 389–400. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8597-0_33.

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Kulich, Miroslav, Petr ŠtŐpán, and Libor Přeučil. "Knowledge Acquisition for Mobile Robot Environment Mapping." In Lecture Notes in Computer Science, 123–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48309-8_11.

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Zelinsky, A. "Environment mapping with a mobile robot using sonar." In AI '88, 363–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52062-7_90.

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Bruno, Giovanni di Dio. "Erwhi Hedgehog: A New Learning Platform for Mobile Robotics." In Makers at School, Educational Robotics and Innovative Learning Environments, 243–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77040-2_32.

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AbstractErwhi Hedgehog is one of the smallest mobile robots. It enables mapping and vision analysis, and also displays machine learning features. Behaving like a small, curious animal, eager to explore the surroundings, the robot can be used to test navigation, mapping and localization algorithms, thus allowing the prototyping of new hardware and software for robotics. This application is particularly handy for educational robotics, at both high school and university level. On the one hand, the project is fully open source and open hardware under MIT license and available on Github, so everyone can build his/her own Erwhi Hedgehog robot with the aid of a step-by-step guide. On the other hand, students with more advanced knowledge can use it as a prototyping platform for developing new software programs and features. Erwhi uses Intel RealSense, AAEON UP Squared and Myriad X VPU technologies, with software based on the Robotic Operating System (ROS), and implements SLAM algorithms, such as RTAB-Map. The machine learning aspect is based on the OpenVINO framework and a dedicated ROS wrapper was used. The software package includes all the programs needed to create a Gazebo simulation. In terms of hardware, motor control is based on an STM32 microcontroller and the Arduino software, and the robot works on the differential drive unicycle model. Finally, Erwhi is compatible with AWS RoboMaker tools.
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Schmidt, Jochen, Chee K. Wong, and Wai K. Yeap. "Localisation and Mapping With a Mobile Robot Using Sparse Range Data." In Autonomous Robots and Agents, 25–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73424-6_4.

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Rogers, John G., Alexander J. B. Trevor, Carlos Nieto-Granda, Alex Cunningham, Manohar Paluri, Nathan Michael, Frank Dellaert, Henrik I. Christensen, and Vijay Kumar. "Effects of Sensory Precision on Mobile Robot Localization and Mapping." In Experimental Robotics, 433–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-28572-1_30.

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Gu, Chaoyue, Zhenxing Sun, and Wang Ting. "Invariant EKF Based Mobile Robot Simultaneous Localization and Mapping Navigation." In Communications in Computer and Information Science, 420–26. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-7946-0_35.

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Piardi, Luis, José Lima, Paulo Costa, and Thadeu Brito. "Development of a Dynamic Path for a Toxic Substances Mapping Mobile Robot in Industry Environment." In ROBOT 2017: Third Iberian Robotics Conference, 655–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70836-2_54.

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Ángel-López, Juan Pablo, and Santiago Murillo-Rendón. "Environment Mapping with Mobile Robot Guided by a Markers Vision System." In Advances in Intelligent Systems and Computing, 149–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61118-1_19.

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Conference papers on the topic "Mobile robot mapping"

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Özkil, Ali Gürcan, and Thomas Howard. "Automatically Annotated Mapping for Indoor Mobile Robot Applications." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-71351.

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This paper presents a new and practical method for mapping and annotating indoor environments for mobile robot use. The method makes use of 2D occupancy grid maps for metric representation, and topology maps to indicate the connectivity of the ‘places-of-interests’ in the environment. Novel use of 2D visual tags allows encoding information physically at places-of-interest. Moreover, using physical characteristics of the visual tags (i.e. paper size) is exploited to recover relative poses of the tags in the environment using a simple camera. This method extends tag encoding to simultaneous localization and mapping in topology space, and fuses camera and robot pose estimations to build an automatically annotated global topo-metric map. It is developed as a framework for a hospital service robot and tested in a real hospital. Experiments show that the method is capable of producing globally consistent, automatically annotated hybrid metric-topological maps that is needed by mobile service robots.
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Kasetani, Misaki, Tomonobu Noguchi, Hirotake Yamazoe, and Joo-Ho Lee. "Projection mapping by mobile projector robot." In 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). IEEE, 2015. http://dx.doi.org/10.1109/urai.2015.7358918.

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Nitsche, Matias, Pablo de Cristoforis, Miroslav Kulich, and Karel Kosnar. "Hybrid mapping for autonomous mobile robot exploration." In 2011 IEEE 6th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS). IEEE, 2011. http://dx.doi.org/10.1109/idaacs.2011.6072761.

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Zhang, Nan, Maohai Li, and Bingrong Hong. "Active Mobile Robot Simultaneous Localization and Mapping." In 2006 IEEE International Conference on Robotics and Biomimetics. IEEE, 2006. http://dx.doi.org/10.1109/robio.2006.340218.

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Liu, Haoxin, Yonghui Zhang, and Yibo Cao. "A Mapping Method for Indoor Mobile Robot." In CSAE 2020: The 4th International Conference on Computer Science and Application Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3424978.3425050.

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Chang, Wen-Chung, and Huan-Chen Ling. "Visual environment mapping for mobile robot navigation." In 2014 10th France-Japan/ 8th Europe-Asia Congress on Mecatronics (MECATRONICS). IEEE, 2014. http://dx.doi.org/10.1109/mecatronics.2014.7018587.

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Milella, Annalisa, Paolo Vanadia, Grazia Cicirelli, and Arcangelo Distante. "Using Passive RFID Technology for Mobile Robot Navigation and Environment Mapping." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41180.

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In this paper, the use of passive Radio Frequency Identification (RFID) as a support technology for mobile robot navigation and environment mapping is investigated. A novel method for localizing passive RFID tags in a geometric map of the environment using fuzzy logic is, first, described. Then, it is shown how a mobile robot equipped with RF antennas, RF reader, and a laser range finder can use such map for localization and path planning. Experimental results from tests performed in our institute suggest that the proposed approach is accurate in mapping RFID tags and can be effectively used for vehicle navigation in indoor environments.
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Ayad, Mustafa, Jun Jason Zhang, Richard Voyles, and Mohammad H. Mahoor. "Mobile robot connectivity maintenance based on RF mapping." In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696840.

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Markom, Marni Azira, Abdul Hamid Adom, Erdy Sulino Mohd Muslim Tan, Shazmin Aniza Abdul Shukor, Norasmadi Abdul Rahim, and Ali Yeon Md Shakaff. "A mapping mobile robot using RP Lidar scanner." In 2015 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS). IEEE, 2015. http://dx.doi.org/10.1109/iris.2015.7451592.

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Fink, Jonathan, and Vijay Kumar. "Online methods for radio signal mapping with mobile robots." In 2010 IEEE International Conference on Robotics and Automation (ICRA 2010). IEEE, 2010. http://dx.doi.org/10.1109/robot.2010.5509574.

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Reports on the topic "Mobile robot mapping"

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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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Brodie, Katherine, Brittany Bruder, Richard Slocum, and Nicholas Spore. Simultaneous mapping of coastal topography and bathymetry from a lightweight multicamera UAS. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41440.

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A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long dwell timeseries of the littoral zone suitable for structure-from motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with root-mean-square error (RMSE)/bias of 0.26/–0.05 and 0.34/–0.05 m, respectively. Bathymetric data from another flight at Virginia Beach, VA, USA, demonstrates successful comparison (RMSE/bias of 0.17/0.06 m) in a secondary environment. UAS-derived engineering data products, total volume profiles and shoreline position, were congruent with those calculated from traditional topo-bathymetric surveys at Duck. Capturing both topography and bathymetry within a single flight, the presented multicamera system is more efficient than data acquisition with a single camera UAS; this advantage grows for longer stretches of coastline (10 km). Efficiency increases further with an on-board Global Navigation Satellite System–Inertial Navigation System (GNSS-INS) to eliminate ground control point (GCP) placement. The Appendix reprocesses the Virginia Beach flight with the GNSS–INS input and no GCPs.
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