A Hardware Approach for Solving the Robot Localization Problem Using a Sequential EKF

2013 ◽  
Author(s):  
Sergio Cruz ◽  
Daniel M. Munoz ◽  
Milton E. Conde ◽  
Carlos H. Llanos ◽  
Geovany A. Borges
Keyword(s):  
Robot Localization ◽  
Localization Problem ◽  
Robot Localization Problem

Uncertainty and imprecision modeling for the mobile robot localization problem

2007 ◽  
Vol 24 (3) ◽  
pp. 267-283 ◽  
Author(s):  
Arnaud Clérentin ◽  
Mélanie Delafosse ◽  
Laurent Delahoche ◽  
Bruno Marhic ◽  
Anne-Marie Jolly-Desodt
Keyword(s):  
Mobile Robot ◽  
Robot Localization ◽  
Localization Problem ◽  
Mobile Robot Localization ◽  
Robot Localization Problem

Constrained and quantized Kalman filtering for an RFID robot localization problem

2010 ◽  
Vol 29 (3-4) ◽  
pp. 235-251 ◽  
Author(s):  
Mauro Boccadoro ◽  
Francesco Martinelli ◽  
Stefano Pagnottelli
Keyword(s):  
Kalman Filtering ◽  
Robot Localization ◽  
Localization Problem ◽  
Robot Localization Problem

scholarly journals A Near-Tight Approximation Algorithm for the Robot Localization Problem

2009 ◽  
Vol 39 (2) ◽  
pp. 461-490 ◽  
Author(s):  
Sven Koenig ◽  
Joseph S. B. Mitchell ◽  
Apurva Mudgal ◽  
Craig Tovey
Keyword(s):  
Approximation Algorithm ◽  
Robot Localization ◽  
Localization Problem ◽  
Robot Localization Problem

The mobile robot localization problem treated as a constraint satisfaction problem

2004 ◽  
Vol 37 (8) ◽  
pp. 394-399
Author(s):  
Mélanie Delafosse ◽  
Arnaud Clerentin ◽  
Laurent Delahoche ◽  
Eric Brassart ◽  
Bruno Marhic
Keyword(s):  
Mobile Robot ◽  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
Robot Localization ◽  
Localization Problem ◽  
Mobile Robot Localization ◽  
Robot Localization Problem

The Robot Localization Problem

1997 ◽  
Vol 26 (4) ◽  
pp. 1120-1138 ◽  
Author(s):  
Leonidas J. Guibas ◽  
Rajeev Motwani ◽  
Prabhakar Raghavan
Keyword(s):  
Robot Localization ◽  
Localization Problem ◽  
Robot Localization Problem

scholarly journals Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2544
Author(s):  
Bin Li ◽  
Yanyang Lu ◽  
Hamid Reza Karimi
Keyword(s):  
Mobile Robot ◽  
Kalman Filtering ◽  
Kinematic Model ◽  
Robot Localization ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Extended Kalman Filtering ◽  
Localization Scheme ◽  
Modeling Errors ◽  
The Impact

In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is [0.0245m0.0224m0.0039rad]T and the average localization errors using the conventional EKF are [1.0405m2.2700m0.1782rad]T, [0.4963m0.3482m0.0254rad]T and [0.2774m0.3897m0.0353rad]T, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

scholarly journals Visual Features Assisted Robot Localization in Symmetrical Environment Using Laser SLAM

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1772
Author(s):  
Gengyu Ge ◽  
Yi Zhang ◽  
Qin Jiang ◽  
Wei Wang
Keyword(s):  
Visual Features ◽  
Robot Localization ◽  
Laser Range ◽  
Geometric Center ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Corner Points ◽  
Monte Carlo Localization ◽  
Robot Localization Problem ◽  
Robot Position

Localization for estimating the position and orientation of a robot in an asymmetrical environment has been solved by using various 2D laser rangefinder simultaneous localization and mapping (SLAM) approaches. Laser-based SLAM generates an occupancy grid map, then the most popular Monte Carlo Localization (MCL) method spreads particles on the map and calculates the position of the robot by a probabilistic algorithm. However, this can be difficult, especially in symmetrical environments, because landmarks or features may not be sufficient to determine the robot’s orientation. Sometimes the position is not unique if a robot does not stay at the geometric center. This paper presents a novel approach to solving the robot localization problem in a symmetrical environment using the visual features-assisted method. Laser range measurements are used to estimate the robot position, while visual features determine its orientation. Firstly, we convert laser range scans raw data into coordinate data and calculate the geometric center. Secondly, we calculate the new distance from the geometric center point to all end points and find the longest distances. Then, we compare those distances, fit lines, extract corner points, and calculate the distance between adjacent corner points to determine whether the environment is symmetrical. Finally, if the environment is symmetrical, visual features based on the ORB keypoint detector and descriptor will be added to the system to determine the orientation of the robot. The experimental results show that our approach can successfully determine the position of the robot in a symmetrical environment, while ordinary MCL and its extension localization method always fail.

scholarly journals Mobile Robot Localization: A Review of Probabilistic Map-Based Techniques

2015 ◽  
Vol 4 (1) ◽  
pp. 73
Author(s):  
Salvador Manuel Malagon-Soldara ◽  
Manuel Toledano-Ayala ◽  
Genaro Soto-Zarazua ◽  
Roberto Valentin Carrillo-Serrano ◽  
Edgar Alejandro Rivas-Araiza
Keyword(s):  
Mobile Robot ◽  
Research Community ◽  
Bayes Rule ◽  
Indoor Environments ◽  
Robot Localization ◽  
Localization Problem ◽  
Comprehensive Review ◽  
Mobile Robot Localization ◽  
Monte Carlo Localization ◽  
Probabilistic Map

This work presents a comprehensive review of current probabilistic developments used to calculate position by mobile robots in indoor environments. In this calculation, best known as localization, it is necessary to develop most of the tasks delegated to the mobile robot. It is then crucial that the methods used for position calculations be as precise as possible, and accurately represent the location of the robot within a given environment. The research community has devoted a considerable amount of time to provide solutions for the localization problem. Several methodologies have been proposed the most common of which are based in the Bayes rule. Other methodologies include the Kalman filter and the Monte Carlo localization filter wich will be addressed in next sections. The major contribution of this review rests in offering a wide array of techniques that researchers can choose. Therefore, method-sensor combinations and their main advantages are displayed.

Sign in / Sign up

Export Citation Format

Share Document