Panoramic representation for route recognition by a mobile robot
International Journal of Computer Vision - Special issue on machine vision research at Osaka University
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Effect of Time-Spatial Size of Motion Image for Localization by Using the Spotting Method
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Image-Based Self-Localization by Means of Zero Phase Representation in Panoramic Images
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Zero Phase Representation of Panoramic Images for Image Vased Localization
CAIP '99 Proceedings of the 8th International Conference on Computer Analysis of Images and Patterns
Real-Time Omnidirectional Image Sensors
International Journal of Computer Vision - Special Issue on Omni-Directional Research in Japan
Iconic Memory-Based Omnidirectional Route Panorama Navigation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Image-Based Localization for AIBO Using Wavelet Transform
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
Robust Appearance Based Visual Route Following for Navigation in Large-scale Outdoor Environments
International Journal of Robotics Research
Image similarity based on Discrete Wavelet Transform for robots with low-computational resources
Robotics and Autonomous Systems
Omni-directional vision for robot navigation in substation environments
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
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This paper proposes a new self-localization method using an omnidirectional image sensor which can observe a surrounding environment with 360-degree of view. The method extracts information which is identical for the position of a sensor and invariant against the rotation of the sensor by generating an autocorrelation image from an observed omnidirectional image. The location of the sensor is estimated by evaluating the similarity among the autocorrelation image of an observed image and stored autocorrelation images. The similarity of autocorrelation images is evaluated in low dimensional eigenspaces generated with stored autocorrelation images. We have conducted experiments with real images and examined the performance of the proposed method. The results show that accurate and robust estimation of the sensor's position is possible with our method.