File Name: localization and mapping of a mobile robot .zip
Simultaneous localization and mapping SLAM is the process by which a mobile robot can construct a map of an unknown environment and simultaneously compute its location using the map 1. SLAM has been formulated and solved as a theoretical problem in many different forms. It has been implemented in several domains from indoor to outdoor, and the possibility of combining robotic in surgery issues has captured the attention of the medical community.
- Simultaneous localization and mapping (SLAM) robotics techniques: a possible application in surgery
- Robotic mapping
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Localization and mapping are the essence of successful navigation in mobile platform technology. Localization is a fundamental task in order to achieve high levels of autonomy in robot navigation and robustness in vehicle positioning. Robot localization and mapping is commonly related to cartography, combining science, technique and computation to build a trajectory map that reality can be modelle Robot localization and mapping is commonly related to cartography, combining science, technique and computation to build a trajectory map that reality can be modelled in ways that communicate spatial information effectively.
Simultaneous localization and mapping (SLAM) robotics techniques: a possible application in surgery
This chapter can be considered as an introduction to mobile robotics. This chapter covers a brief mathematical description of mobile robots that consists of kinematic and dynamics with nonholonomic constrains applied to wheeled robots. Then, a terrain representation and mapping survey has been conducted. After that, the path planning study was performed. It includes a planner definition and several approaches to navigation, which are divided by different behaviors and types of world representation.
Robotic mapping is a discipline related to computer vision  and cartography. The goal for an autonomous robot is to be able to construct or use a map outdoor use or floor plan indoor use and to localize itself and its recharging bases or beacons in it. Evolutionarily shaped blind action may suffice to keep some animals alive. For some insects for example, the environment is not interpreted as a map, and they survive only with a triggered response. A slightly more elaborated navigation strategy dramatically enhances the capabilities of the robot.
Skip to Main Content. A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. Use of this web site signifies your agreement to the terms and conditions. Graph-based robust localization and mapping for autonomous mobile robotic navigation Abstract: Simultaneous Localization and Mapping SLAM means to estimate the positions and orientations of the mobile robot and to construct the model of the environment, essential and critical for autonomous navigation and widely used in a large range of application fields, the research goal is to design, implement and validate graph-based robust SLAM algorithm in indoor office-like dynamic scenarios. On the local level, scan matching is executed to estimate the local-relative-roto-translation value: first, pre-processing is performed to filter out the parts corresponding to the moving objects in the raw LIDAR data; second, conditioned-hough-transform-and-linear-regression-based line-segment detection is accomplished to detect the line features from the rest of LIDAR data; third, matching by fitting point to line is applied to estimate the roto-translation value. On the global level, the topological graph is constructed with the previously estimated motion constraints and batch optimization is achieved by a linear solution to estimate the global robot trajectory. Meanwhile, for the local line-feature maps which includes information about the static environment, they are transformed to the global frame based on the robot-pose information and integrated to construct the global-line-feature map.
As mobile robots become more common in general knowledge and practices, as opposed to simply in research labs, there is an increased need for the introduction and methods to Simultaneous Localization and Mapping SLAM and its techniques and concepts related to robotics. Simultaneous Localization and Mapping for Mobile Robots: Introduction and Methods investigates the complexities of the theory of probabilistic localization and mapping of mobile robots as well as providing the most current and concrete developments. This reference source aims to be useful for practitioners, graduate and postgraduate students, and active researchers alike. Fernandez-Madrigal and Claraco both U. Writing for practitioners and graduate students, they cover robotic, probabilistic, and statistical basics; robot motion and sensor models; mobile robot localization with recursive Bayesian filters; types and constructions of maps for mobile robots; the Bayesian approach to simultaneous localization and mapping SLAM ; and advanced SLAM techniques. Buy Hardcover. Add to Cart.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Pfister Published Computer Science. Mobile robot localization and mapping in unknown environments is a fundamental requirement for effective autonomous navigation. Three different approaches to localization and mapping are presented. Each is based on data collected from a robot using a dense range scanner to generate a planar representation of the surrounding environment.
In this proposed method, the tracking and mapping procedures are split into two separate tasks and performed in parallel threads. In the tracking thread, a ground feature-based pose estimation method is employed to initialize the algorithm for the constraint moving of the mobile robot. And an initial map is built by triangulating the matched features for further tracking procedure. In the mapping thread, an epipolar searching procedure is utilized for finding the matching features. A homography-based outlier rejection method is adopted for rejecting the mismatched features. The indoor experimental results demonstrate that the proposed algorithm has a great performance on map building and verify the feasibility and effectiveness of the proposed algorithm. To successfully accomplish autonomous or semiautonomous tasks to assist daily human activities, building the 3D geometry map of the environment is becoming one of the fundamental issues for mobile robotics.
Mobile robot localization and mapping in unknown environments is a fundamental require- ment for accuracy of any sensor-based localization and mapping method. bobsnail.org∼ stergios/tech reports/tr wlsm bobsnail.org
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