Particle Filter Localization Using Visual Markers Based Omnidirectional Vision and a Laser Sensor

2019 ◽  
Author(s):  
Patricia Javierre ◽  
Biel Piero Alvarado ◽  
Paloma de la Puente
Keyword(s):  
Particle Filter ◽  
Omnidirectional Vision ◽  
Laser Sensor ◽  
Visual Markers

Localization of mobile robots with omnidirectional vision using Particle Filter and iterative SIFT

2006 ◽  
Vol 54 (9) ◽  
pp. 758-765 ◽  
Author(s):  
Hashem Tamimi ◽  
Henrik Andreasson ◽  
André Treptow ◽  
Tom Duckett ◽  
Andreas Zell
Keyword(s):  
Mobile Robots ◽  
Particle Filter ◽  
Omnidirectional Vision

scholarly journals Particle Filter and Finite Impulse Response Filter Fusion and Hector SLAM to Improve the Performance of Robot Positioning

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Amin Bassiri ◽  
Mohammadreza Asghari Oskoei ◽  
Anahid Basiri
Keyword(s):  
Particle Filter ◽  
Impulse Response ◽  
Hybrid Algorithm ◽  
Finite Impulse Response ◽  
Sampling Rate ◽  
Position Estimation ◽  
Laser Sensor ◽  
Indoor Environments ◽  
Hybrid Filter ◽  
Signal Ratio

Indoor position estimation is essential for navigation; however, it is a challenging task mainly due to the indoor environments’ (a) high noise to signal ratio and (b) low sampling rate and (c) sudden changes to the environments. This paper uses a hybrid filter algorithm for the indoor positioning system for robot navigation integrating Particle Filter (PF) algorithm and Finite Impulse Response (FIR) filter algorithm to assure the continuity of the positioning solution. Additionally, the Hector Simultaneous Localisation and Mapping (Hector SLAM) algorithm is used to map the environment and improve the accuracy of the navigation. The paper implements the hybrid algorithm that uses the integrated PF, FIR, and Hector SLAM, using an embedded laser scanner sensor. The hybrid algorithm coupled with Hector SLAM is tested in several scenarios to evaluate the performance of the system, in terms of continuity and accuracy of the position estimation, and compares it with similar systems. The scenarios where the system is tested include reducing the laser sensor readings (low sampling rate), dynamic environments (change in the location of the obstacles), and the kidnapped robot situation. The results show that the system provides a significantly better accuracy and continuity of the position estimation in all scenarios, even in comparison with similar hybrid systems, except where there is a high and constant noise, where the performance of the hybrid filter and the simple PF seems almost the same.

Omnidirectional Vision Based Mobile Robot Hierarchical SLAM

2011 ◽  
Vol 464 ◽  
pp. 95-98
Author(s):  
Mao Hai Li ◽  
Li Ning Sun ◽  
Ming Qiang Pan
Keyword(s):  
Mobile Robot ◽  
Particle Filter ◽  
Position Estimation ◽  
Medium Size ◽  
Accurate Estimation ◽  
Omnidirectional Vision ◽  
Alignment Algorithm ◽  
Feature Maps ◽  
Topological Graph ◽  
Local Map

A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps of large environments is proposed. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF). The particle filter is used to extend the path posterior by sampling new poses that integrate the current observation which drastically reduces the uncertainty about the robot pose. The landmark position estimation and update is also implemented through Kalman filter. Omnidirectional vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching Scale Invariant Feature Transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-Tree. Experimental results on real robot in a medium size, real indoor environment show the practicality and efficiency of our proposed method.

scholarly journals Rotational Kinematics Model Based Adaptive Particle Filter for Robust Human Tracking in Thermal Omnidirectional Vision

2015 ◽  
Vol 2015 ◽  
pp. 1-11
Author(s):  
Yazhe Tang ◽  
Jun Luo ◽  
Y. F. Li ◽  
Xiaolong Zhou
Keyword(s):  
Particle Filter ◽  
Vision System ◽  
Image Plane ◽  
Human Tracking ◽  
Omnidirectional Vision ◽  
Robust Tracking ◽  
Rotational Kinematics ◽  
Equivalent Sphere ◽  
Thermal Vision ◽  
Adaptive Neighborhood

This paper presents a novel surveillance system named thermal omnidirectional vision (TOV) system which can work in total darkness with a wild field of view. Different to the conventional thermal vision sensor, the proposed vision system exhibits serious nonlinear distortion due to the effect of the quadratic mirror. To effectively model the inherent distortion of omnidirectional vision, an equivalent sphere projection is employed to adaptively calculate parameterized distorted neighborhood of an object in the image plane. With the equivalent projection based adaptive neighborhood calculation, a distortion-invariant gradient coding feature is proposed for thermal catadioptric vision. For robust tracking purpose, a rotational kinematic modeled adaptive particle filter is proposed based on the characteristic of omnidirectional vision, which can handle multiple movements effectively, including the rapid motions. Finally, the experiments are given to verify the performance of the proposed algorithm for human tracking in TOV system.

scholarly journals A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
KyungJae Ahn ◽  
Yeonsik Kang
Keyword(s):  
Particle Filter ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Laser Sensor ◽  
Laser Range Finder ◽  
Reasonable Accuracy ◽  
Positioning Systems ◽  
Localization Method ◽  
Global Positioning ◽  
Grid Based

This paper presents a method of particle filter localization for autonomous vehicles, based on two-dimensional (2D) laser sensor measurements and road features. To navigate an urban environment, an autonomous vehicle should be able to estimate its location with a reasonable accuracy. By detecting road features such as curbs and road markings, a grid-based feature map is constructed using 2D laser range finder measurements. Then, a particle filter is employed to accurately estimate the position of the autonomous vehicle. Finally, the performance of the proposed method is verified and compared to accurate Differential Global Positioning Systems (DGPS) data through real road driving experiments.

scholarly journals Camera Space Particle Filter for the Robust and Precise Indoor Localization of a Wheelchair

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Raul Chavez-Romero ◽  
Antonio Cardenas ◽  
Mauro Maya ◽  
Alejandra Sanchez ◽  
Davide Piovesan
Keyword(s):  
Particle Filter ◽  
Indoor Localization ◽  
Theoretical Development ◽  
Comparative Performance ◽  
Vision Sensors ◽  
Experimental Implementation ◽  
Visual Markers ◽  
Order Of Magnitude ◽  
Observation Function ◽  
Robotic Wheelchair

This paper presents the theoretical development and experimental implementation of a sensing technique for the robust and precise localization of a robotic wheelchair. Estimates of the vehicle’s position and orientation are obtained, based on camera observations of visual markers located at discrete positions within the environment. A novel implementation of a particle filter on camera sensor space (Camera-Space Particle Filter) is used to combine visual observations with sensed wheel rotations mapped onto a camera space through an observation function. The camera space particle filter fuses the odometry and vision sensors information within camera space, resulting in a precise update of the wheelchair’s pose. Using this approach, an inexpensive implementation on an electric wheelchair is presented. Experimental results within three structured scenarios and comparative performance using an Extended Kalman Filter (EKF) and Camera-Space Particle Filter (CSPF) implementations are discussed. The CSPF was found to be more precise in the pose of the wheelchair than the EKF since the former does not require the assumption of a linear system affected by zero-mean Gaussian noise. Furthermore, the time for computational processing for both implementations is of the same order of magnitude.

A mutual information‐based likelihood function for particle filter flood extent assimilation

2021 ◽  
Author(s):  
Antara Dasgupta ◽  
Renaud Hostache ◽  
RAAJ Ramasankaran ◽  
Guy J.‐P Schumann ◽  
Stefania Grimaldi ◽  
...  
Keyword(s):  
Mutual Information ◽  
Particle Filter ◽  
Likelihood Function
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