A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry and texture recovery of scenes of large scale
Computer Vision and Image Understanding
Exploring artificial intelligence in the new millennium
Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
A New Paradigm for Recognizing 3-D Object Shapes from Range Data
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fully Automatic Registration of 3D Point Clouds
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Multiview registration for large data sets
3DIM'99 Proceedings of the 2nd international conference on 3-D digital imaging and modeling
| Hi-index | 0.00 |
Autonomous robotic mapping has been an open research topic for more than twenty years. The primary objective of the robotic mapping problem is to design methods that can guide a robot around an environment and allow it to create a map of what has been sensed. Most automatic mapping algorithms rely on robot pose estimation to fuse map data together. This paper demonstrates that through feature extraction using spin-histograms, the pose of the robot can be estimated accurately enough for an Iterative Closest Point (ICP) algorithm to register overlapping data sets. By eliminating consideration for points according to curvature and saliency, the spin-histogram matching process can improve in both accuracy and computation time. In combination with a global registration algorithm known as simultaneous matching, this process can obtain a fully autonomous registration process.