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Wyszukujesz frazę "simultaneous mapping" wg kryterium: Temat


Wyświetlanie 1-9 z 9
Tytuł:
Efficient RGB-D data processing for feature-based self-localization of mobile robots
Autorzy:
Kraft, M.
Nowicki, M.
Penne, R.
Schmidt, A.
Skrzypczyński, P.
Tematy:
visual odometry
simultaneous localization
simultaneous mapping
RGB-D
tracking
point features
odometria wizyjna
lokalizacja jednoczesna
śledzenie
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Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:
https://bibliotekanauki.pl/articles/330295.pdf  Link otwiera się w nowym oknie
Opis:
The problem of position and orientation estimation for an active vision sensor that moves with respect to the full six degrees of freedom is considered. The proposed approach is based on point features extracted from RGB-D data. This work focuses on efficient point feature extraction algorithms and on methods for the management of a set of features in a single RGB-D data frame. While the fast, RGB-D-based visual odometry system described in this paper builds upon our previous results as to the general architecture, the important novel elements introduced here are aimed at improving the precision and robustness of the motion estimate computed from the matching point features of two RGB-D frames. Moreover, we demonstrate that the visual odometry system can serve as the front-end for a pose-based simultaneous localization and mapping solution. The proposed solutions are tested on publicly available data sets to ensure that the results are scientifically verifiable. The experimental results demonstrate gains due to the improved feature extraction and management mechanisms, whereas the performance of the whole navigation system compares favorably to results known from the literature.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An ant-based filtering random-finite-set approach to simultaneous localization and mapping
Autorzy:
Li, D.
Zhu, J.
Xu, B.
Lu, M.
Li, M.
Tematy:
simultaneous localization
simultaneous mapping
random finite set
probability hypothesis density
ant colony
lokalizacja jednoczesna
mapowanie jednoczesne
algorytm mrówkowy
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Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:
https://bibliotekanauki.pl/articles/329854.pdf  Link otwiera się w nowym oknie
Opis:
Inspired by ant foraging, as well as modeling of the feature map and measurements as random finite sets, a novel formulation in an ant colony framework is proposed to jointly estimate the map and the vehicle trajectory so as to solve a feature-based simultaneous localization and mapping (SLAM) problem. This so-called ant-PHD-SLAM algorithm allows decomposing the recursion for the joint map-trajectory posterior density into a jointly propagated posterior density of the vehicle trajectory and the posterior density of the feature map conditioned on the vehicle trajectory. More specifically, an ant-PHD filter is proposed to jointly estimate the number of map features and their locations, namely, using the powerful search ability and collective cooperation of ants to complete the PHD-SLAM filter time prediction and data update process. Meanwhile, a novel fast moving ant estimator (F-MAE) is utilized to estimate the maneuvering vehicle trajectory. Evaluation and comparison using several numerical examples show a performance improvement over recently reported approaches. Moreover, the experimental results based on the robot operation system (ROS) platform validate the consistency with the results obtained from numerical simulations.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Visual simultaneous localisation and mapping methodologies
Autorzy:
Bouhamatou, Zoulikha
Abdessemed, Foudil
Tematy:
simultaneous localisation and mapping
SLAM
visual SLAM
deep-learning SLAM
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Wydawca:
Politechnika Białostocka. Oficyna Wydawnicza Politechniki Białostockiej
Powiązania:
https://bibliotekanauki.pl/articles/58907248.pdf  Link otwiera się w nowym oknie
Opis:
Simultaneous localisation and mapping (SLAM) is a process by which robots build maps of their environment and simultaneous-ly determine their location and orientation in the environment. In recent years, SLAM research has advanced quickly. Researchers are cur-rently working on developing reliable and accurate visual SLAM algorithms dealing with dynamic environments. The steps involved in de-veloping a SLAM system are described in this article. We explore the most-recent methods used in SLAM systems, including probabilistic methods, visual methods, and deep learning (DL) methods. We also discuss the fundamental techniques utilised in SLAM fields.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Simultaneous localization and mapping: A feature-based probabilistic approach
Autorzy:
Skrzypczyński, P.
Tematy:
robot mobilny
lokalizacja równoczesna
dopasowanie właściwości
mobile robot
simultaneous localization and mapping
feature matching
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Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:
https://bibliotekanauki.pl/articles/929972.pdf  Link otwiera się w nowym oknie
Opis:
This article provides an introduction to Simultaneous Localization And Mapping (SLAM), with the focus on probabilistic SLAM utilizing a feature-based description of the environment. A probabilistic formulation of the SLAM problem is introduced, and a solution based on the Extended Kalman Filter (EKF-SLAM) is shown. Important issues of convergence, consistency, observability, data association and scaling in EKF-SLAM are discussed from both theoretical and practical points of view. Major extensions to the basic EKF-SLAM method and some recent advances in SLAM are also presented.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Homography augmented particle filter SLAM
Autorzy:
Słowak, Paweł Leszek
Kaniewski, Piotr
Tematy:
Simultaneous Localization and Mapping
SLAM
homography matrix
particle filter
robot navigation
visual-inertial systems
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Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Powiązania:
https://bibliotekanauki.pl/articles/27311746.pdf  Link otwiera się w nowym oknie
Opis:
The article presents a comprehensive study of a visual-inertial simultaneous localization and mapping (SLAM) algorithm designed for aerial vehicles. The goal of the research is to propose an improvement to the particle filter SLAM system that allows for more accurate and robust navigation of unknown environments. The authors introduce a modification that utilizes a homography matrix decomposition calculated from the camera frame-to-frame relationships. This procedure aims to refine the particle filter proposal distribution of the estimated robot state. In addition, the authors implement a mechanism of calculating a homography matrix from robot displacement, which is utilized to eliminate outliers in the frame-to-frame feature detection procedure. The algorithm is evaluated using simulation and real-world datasets, and the results show that the proposed improvements make the algorithm more accurate and robust. Specifically, the use of homography matrix decomposition allows the algorithm to be more efficient, with a smaller number of particles, without sacrificing accuracy. Furthermore, the incorporation of robot displacement information helps improve the accuracy of the feature detection procedure, leading to more reliable and consistent results. The article concludes with a discussion of the implemented and tested SLAM solution, highlighting its strengths and limitations. Overall, the proposed algorithm is a promising approach for achieving accurate and robust autonomous navigation of unknown environments.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Simultaneous localization and mapping for tracked wheel robots combining monocular and stereo vision
Autorzy:
Jesus, F.
Ventura, R.
Tematy:
simultaneous localisation and mapping
extended Kalman filter
feature detector
inverse depth parametrization
landmark evaluation
temporal difference learning
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Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:
https://bibliotekanauki.pl/articles/384393.pdf  Link otwiera się w nowym oknie
Opis:
This paper addresses an online 6D SLAM method for a tracked wheel robot in an unknown and unstructured environment. While the robot pose is represented by its position and orientation over a 3D space, the environment is mapped with natural landmarks in the same space, autonomously collected using visual data from feature detectors. The observation model employs opportunistically features detected from either monocular and stereo vision. These features are represented using an inverse depth parametrization. The motion model uses odometry readings from motor encoders and orientation changes measured with an IMU. A dimensional-bounded EKF (DBEKF) is introduced here, that keeps the dimension of the state bounded. A new landmark classifier using a Temporal Difference Learning methodology is used to identify undesired landmarks from the state. By forcing an upper bound to the number of landmarks in the EKF state, the computational complexity is reduced to up to a constant while not compromising its integrity. All experimental work was done using real data from RAPOSA-NG, a tracked wheel robot developed for Search and Rescue missions.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Straight-lines modelling using planar information for monocular SLAM
Autorzy:
Santana, A. M.
Medeiros, A. A. D.
Tematy:
SLAM
filtr Kalmana
transformata Hough'a
Simultaneous Localization and Mapping (SLAM)
Kalman filter
Hough transform
monocular vision
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Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Powiązania:
https://bibliotekanauki.pl/articles/331312.pdf  Link otwiera się w nowym oknie
Opis:
This work proposes a SLAM (Simultaneous Localization And Mapping) solution based on an Extended Kalman Filter (EKF) in order to enable a robot to navigate along the environment using information from odometry and pre-existing lines on the floor. These lines are recognized by a Hough transform and are mapped into world measurements using a homography matrix. The prediction phase of the EKF is developed using an odometry model of the robot, and the updating makes use of the line parameters in Kalman equations without any intermediate stage for calculating the distance or the position. We show two experiments (indoor and outdoor) dealing with a real robot in order to validate the project.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
SLAM – Based Approach to Dynamic Ship Positioning
Autorzy:
Wróbel, K. A.
Tematy:
Dynamic Ship Positioning
Simultaneous Localization and Mapping (SLAM)
Dynamic Positioning (DP)
Reference System
SLAM Method
SLAM Post-Processing
Doppler Velocity Log (DVL)
Hydroacoustics
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Wydawca:
Uniwersytet Morski w Gdyni. Wydział Nawigacyjny
Powiązania:
https://bibliotekanauki.pl/articles/116689.pdf  Link otwiera się w nowym oknie
Opis:
Dynamically positioned vessels, used by offshore industry, use not only satellite navigation but also different positioning systems, often referred to as ‘reference’ systems. Most of them use multiple technical devices located outside the vessel which creates some problems with their accessibility and performance. In this paper, a basic concept of reference system independent from any external device is presented, basing on hydroacoustics and Simultaneous Localization and Mapping (SLAM) method. Theoretical analysis of its operability is also performed.
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Simultaneous localization and mapping of a mobile robot with stereo camera using ORB features
Autorzy:
Raoui, Younès
Amraoui, Mohammed
Tematy:
simultaneous localization
mapping
stereo camera
extended Kalman filter
mobile robots
navigation
Pokaż więcej
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:
https://bibliotekanauki.pl/articles/59496191.pdf  Link otwiera się w nowym oknie
Opis:
Simultaneous Localization and Mapping (SLAM) is applied to robots for accurate navigation. The stereo cameras are suitable for visual SLAM as they can give the depth of the visual landmarks and more precise estimations of the robot’s pose. In this paper, we present a survey of SLAM methods, either Bayesian or bioinspired. Then we present a new method of SLAM, which we call stereo Extended Kalman Filter, improving the matching by computing the innovation matrices from the left and the right images. The landmarks are computed from Oriented FAST and Rotated BRIEF (ORB) features for detecting salient points and their descriptors. The covariance matrices of the state and the robot’s map are reduced during the robot’s motion. Experiments are done on the raw images of the Kitti dataset.
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-9 z 9

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