Dynamic Omnidirectional Vision Localization Using a Beacon Tracker Based on Particle Filter

Omni-directional Vision Localization Based on Particle Filter

Fourth International Conference on Image and Graphics (ICIG 2007) ◽

10.1109/icig.2007.145 ◽

2007 ◽

Cited By ~ 12

Author(s):

Zuoliang Cao ◽

Shiyu Liu ◽

Juha Roning

Keyword(s):

Particle Filter ◽

Vision Localization

Download Full-text

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

Robotics and Autonomous Systems ◽

10.1016/j.robot.2006.04.018 ◽

2006 ◽

Vol 54 (9) ◽

pp. 758-765 ◽

Cited By ~ 45

Author(s):

Hashem Tamimi ◽

Henrik Andreasson ◽

André Treptow ◽

Tom Duckett ◽

Andreas Zell

Keyword(s):

Mobile Robots ◽

Particle Filter ◽

Omnidirectional Vision

Download Full-text

Omnidirectional Vision Based Mobile Robot Hierarchical SLAM

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.464.95 ◽

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.

Download Full-text

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

Mathematical Problems in Engineering ◽

10.1155/2015/347497 ◽

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.

Download Full-text