1,081 research outputs found

    Simple yet stable bearing-only navigation

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    This article describes a simple monocular navigation system for a mobile robot based on the map-and-replay technique. The presented method is robust and easy to implement and does not require sensor calibration or structured environment, and its computational complexity is independent of the environment size. The method can navigate a robot while sensing only one landmark at a time, making it more robust than other monocular approaches. The aforementioned properties of the method allow even low-cost robots to effectively act in large outdoor and indoor environments with natural landmarks only. The basic idea is to utilize a monocular vision to correct only the robot's heading, leaving distance measurements to the odometry. The heading correction itself can suppress the odometric error and prevent the overall position error from diverging. The influence of a map-based heading estimation and odometric errors on the overall position uncertainty is examined. A claim is stated that for closed polygonal trajectories, the position error of this type of navigation does not diverge. The claim is defended mathematically and experimentally. The method has been experimentally tested in a set of indoor and outdoor experiments, during which the average position errors have been lower than 0.3 m for paths more than 1 km long

    Consistent Map Building Based on Sensor Fusion for Indoor Service Robot

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    Extended Kalman Filter Implementation for the Khepera II Mobile Robot

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    The accurate estimation of robot position and orientation in real-time is one of the fundamental challenges in mobile robotics. The Extended Kalman Filter is a nonlinear real-time recursive time domain ïŹlter that combines available sensor data to produce an accurate estimate of state, and has been successfully applied to the localization problem in mobile robotics and aircraft navigation. This report describes an Extended Kalman Filter implementa- tion for the Khepera II mobile robotics platform that seeks to produce accurate localization estimates in real-time using wheel odometry data, IR sensor range data, and compass heading data

    CES-515 Towards Localization and Mapping of Autonomous Underwater Vehicles: A Survey

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    Autonomous Underwater Vehicles (AUVs) have been used for a huge number of tasks ranging from commercial, military and research areas etc, while the fundamental function of a successful AUV is its localization and mapping ability. This report aims to review the relevant elements of localization and mapping for AUVs. First, a brief introduction of the concept and the historical development of AUVs is given; then a relatively detailed description of the sensor system used for AUV navigation is provided. As the main part of the report, a comprehensive investigation of the simultaneous localization and mapping (SLAM) for AUVs are conducted, including its application examples. Finally a brief conclusion is summarized

    Building maps of large environments using splines and geometric analysis

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    Recently, a novel solution to the Simultaneous Localization and Map building (SLAM) problem for complex indoor environments was presented, using a set of splines for describing the geometries detected by a laser range finder mounted on a mobile platform. In this paper, a method for exploiting the geometric information underlying in these maps in the data association process is described. The proposed approach uses graphs of relations between simple features extracted from the environment, and a bit encoded implementation for obtaining a maximum clique that relates observations with previously visited areas. This information is used to update the relative positions of a collage of submaps of limited size

    Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

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    Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved

    Cost-effective robot for steep slope crops monitoring

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    This project aims to develop a low cost, simple and robust robot able to autonomously monitorcrops using simple sensors. It will be required do develop robotic sub-systems and integrate them with pre-selected mechanical components, electrical interfaces and robot systems (localization, navigation and perception) using ROS, for wine making regions and maize fields

    Cooperative simultaneous localization and mapping framework

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    This research work is a contribution to develop a framework for cooperative simultaneous localization and mapping with multiple heterogeneous mobile robots. The presented research work contributes in two aspects of a team of heterogeneous mobile robots for cooperative map building. First it provides a mathematical framework for cooperative localization and geometric features based map building. Secondly it proposes a software framework for controlling, configuring and managing a team of heterogeneous mobile robots. Since mapping and pose estimation are very closely related to each other, therefore, two novel sensor data fusion techniques are also presented, furthermore, various state of the art localization and mapping techniques and mobile robot software frameworks are discussed for an overview of the current development in this research area. The mathematical cooperative SLAM formulation probabilistically solves the problem of estimating the robots state and the environment features using Kalman filter. The software framework is an effort toward the ongoing standardization process of the cooperative mobile robotics systems. To enhance the efficiency of a cooperative mobile robot system the proposed software framework addresses various issues such as different communication protocol structure for mobile robots, different sets of sensors for mobile robots, sensor data organization from different robots, monitoring and controlling robots from a single interface. The present work can be applied to number of applications in various domains where a priori map of the environment is not available and it is not possible to use global positioning devices to find the accurate position of the mobile robot. Therefore the mobile robot(s) has to rely on building the map of its environment and using the same map to find its position and orientation relative to the environment. The exemplary areas for applying the proposed SLAM technique are Indoor environments such as warehouse management, factory floors for parts assembly line, mapping abandoned tunnels, disaster struck environment which are missing maps, under see pipeline inspection, ocean surveying, military applications, planet exploration and many others. These applications are some of many and are only limited by the imagination.Diese Forschungsarbeit ist ein Beitrag zur Entwicklung eines Framework fĂŒr kooperatives SLAM mit heterogenen, mobilen Robotern. Die prĂ€sentierte Forschungsarbeit trĂ€gt in zwei Aspekten in einem Team von heterogenen, mobilen Robotern bei. Erstens stellt es einen mathematischen Framework fĂŒr kooperative Lokalisierung und geometrisch basierende Kartengenerierung bereit. Zweitens schlĂ€gt es einen Softwareframework zur Steuerung, Konfiguration und Management einer Gruppe von heterogenen mobilen Robotern vor. Da Kartenerstellung und PoseschĂ€tzung miteinander stark verbunden sind, werden zwei neuartige Techniken zur Sensordatenfusion prĂ€sentiert. Weiterhin werden zum Stand der Technik verschiedene Techniken zur Lokalisierung und Kartengenerierung sowie Softwareframeworks fĂŒr die mobile Robotik diskutiert um einen Überblick ĂŒber die aktuelle Entwicklung in diesem Forschungsbereich zu geben. Die mathematische Formulierung des SLAM Problems löst das Problem der RoboterzustandsschĂ€tzung und der Umgebungmerkmale durch Benutzung eines Kalman filters. Der Softwareframework ist ein Beitrag zum anhaltenden Standardisierungsprozess von kooperativen, mobilen Robotern. Um die EffektivitĂ€t eines kooperativen mobilen Robotersystems zu verbessern enthĂ€lt der vorgeschlagene Softwareframework die Möglichkeit die Kommunikationsprotokolle flexibel zu Ă€ndern, mit verschiedenen Sensoren zu arbeiten sowie die Möglichkeit die Sensordaten verschieden zu organisieren und verschiedene Roboter von einem Interface aus zu steuern. Die prĂ€sentierte Arbeit kann in einer Vielzahl von Applikationen in verschiedenen DomĂ€nen benutzt werden, wo eine Karte der Umgebung nicht vorhanden ist und es nicht möglich ist GPS Daten zur prĂ€zisen Lokalisierung eines mobilen Roboters zu nutzen. Daher mĂŒssen die mobilen Roboter sich auf die selbsterstellte Karte verlassen und die selbe Karte zur Bestimmung von Position und Orientierung relativ zur Umgebung verwenden. Die exemplarischen Anwendungen der vorgeschlagenen SLAM Technik sind Innenraumumgebungen wie Lagermanagement, FabrikgebĂ€ude mit ProduktionsstĂ€tten, verlassene Tunnel, Katastrophengebiete ohne aktuelle Karte, Inspektion von Unterseepipelines, Ozeanvermessung, MilitĂ€ranwendungen, Planetenerforschung und viele andere. Diese Anwendungen sind einige von vielen und sind nur durch die Vorstellungskraft limitiert
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