Estimation of Object Motion Parameters from Noisy Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introductory Techniques for 3-D Computer Vision
Introductory Techniques for 3-D Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interpreting Face Images Using Active Appearance Models
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Probabilistic Object Tracking Using Multiple Features
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
ACM Computing Surveys (CSUR)
Visual mapping by a robot rover
IJCAI'79 Proceedings of the 6th international joint conference on Artificial intelligence - Volume 1
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Accurate robot detection and localisation is fundamental in applications which involve robot navigation. Typical methods for robot detection require a model of a robot. However in most applications the availability of such model can not be warranted. This paper discusses a different approach. A method is presented to localise the robot in a complex and dynamic scene based only on the information that the robot is following a previously specified movement pattern. The advantage of this method lies in the ability to detect differently shaped and differently looking robots as long as they perform the previously defined movement. The method has been successfully tested in an indoor environment.