A Method of Landmark Visual Tracking for Mobile Robot

Authors:
Lijun Zhao;Ruifeng Li;Tianying Zang;Lining Sun;Xufeng Fan
Affiliations:
State Key Laboratory of Advanced Robotic Technology and System, Harbin, China;State Key Laboratory of Advanced Robotic Technology and System, Harbin, China;State Key Laboratory of Advanced Robotic Technology and System, Harbin, China;State Key Laboratory of Advanced Robotic Technology and System, Harbin, China;State Key Laboratory of Advanced Robotic Technology and System, Harbin, China
Venue:
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
Year:
2008

Citing 12
Cited 1

In Defense of the Eight-Point Algorithm

IEEE Transactions on Pattern Analysis and Machine Intelligence
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Mean Shift, Mode Seeking, and Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Indexing Using Approximate Nearest-Neighbour Search in High-Dimensional Spaces

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Shape Matching and Object Recognition Using Low Distortion Correspondences

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors

IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards automatic visual obstacle avoidance

IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
PCA-SIFT: a more distinctive representation for local image descriptors

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing

Simultaneous Visual Object Recognition and Position Estimation Using SIFT

ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

Landmark tracking is key factor for mobile robots localization and navigation. This paper proposes a combined approach automatically to detect and track landmark. Firstly, a landmark is initially located in the image coordinates by features recognition- SIFT (Scale Invariant Feature Transform) and matching technology-RANSAC(Random Sample Consensus). Then based on similarity distance, tracking algorithm is called, which depends on adaptive particle filter. Furthermore, re-position strategy based SIFT is also presented to catch the landmark which was lost. Finally, the experimental results show that the proposed method achieves robust and real-time tracking of a landmark and has a practical value for robot visual.