View-based robot localization using spherical harmonics: concept and first experimental results

Authors:
Holger Friedrich;David Dederscheck;Kai Krajsek;Rudolf Mester
Affiliations:
Visual Sensorics and Information Processing Lab, J.W. Goethe University, Frankfurt, Germany;Visual Sensorics and Information Processing Lab, J.W. Goethe University, Frankfurt, Germany;Visual Sensorics and Information Processing Lab, J.W. Goethe University, Frankfurt, Germany;Visual Sensorics and Information Processing Lab, J.W. Goethe University, Frankfurt, Germany
Venue:
Proceedings of the 29th DAGM conference on Pattern recognition
Year:
2007

Citing 6
Cited 1

Regular Article: Computing Fourier Transforms and Convolutions on the 2-Sphere

Advances in Applied Mathematics
Determination of the orientation of 3D objects using spherical harmonics

Graphical Models and Image Processing
Rotation invariant spherical harmonic representation of 3D shape descriptors

Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Rotation Estimation from Spherical Images

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Rotation Recovery from Spherical Images without Correspondences

IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction of haar integral features on omnidirectional images: application to local and global localization

DAGM'06 Proceedings of the 28th conference on Pattern Recognition

Optical Rails

Proceedings of the 30th DAGM symposium on Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Robot self-localization using a hemispherical camera system can be done without correspondences. We present a view-based approach using view descriptors, which enables us to efficiently compare the image signal taken at different locations. A compact representation of the image signal can be computed using Spherical Harmonics as orthonormal basis functions defined on the sphere. This is particularly useful because rotations between two representations can be found easily. Compact view descriptors stored in a database enable us to compute a likelihood for the current view corresponding to a particular position and orientation in the map.